CLEAR Journal September 2017 by Simple Groups

CLEAR SEPTEMBER 2017

CLEAR Journal (Computational Linguistics in Engineering And Research) M. Tech Computational Linguistics, Dept. of Computer Science and Engineering, Govt. Engineering College, Sreekrishnapuram, Palakkad678633 www.simplegroups.in simplequest.in@gmail.com

Editorial………………………………………… 4

Chief Editor Shine S Assistant Professor Dept. of Computer Science and Engineering Govt. Engineering College, Sreekrishnapuram, Palakkad678633

Common sense reasoning........................................05

CLEAR December 2017 Invitation………………………………………21 Last word………………………………………22

Nayana R M Human Computer Dialogue Systems………...............................09 Jenny George

Editors Ayishathahira C H Manjusha P D Rahul M Sreelakshmi K Cover page and Layout Rahul M

Lexical Simplification................................14 Silpa K S Sort Story....................................18 Shabna Nasser, Fathima Shabana K, Fathima Shirin A, Fathima Riswana K

CLEAR SEPTEMBER 2017

Dear Readers! Greetings! Dear Readers, This edition of CLEAR Journal contains articles about some interesting topics like Common sense reasoning, Human Computer Dialogue Systems, Lexical Simplification and Sorting of Jumbled Story lines. In our last edition we primarily focus on researches and works done related to some trending topics like Text to Image Synthesis by Using GAN, Metaphor Processing, Malayalam Word Sense Disambiguation Using Naive Bayes Classifier and Zero Shot Translation by Googleâ&#x20AC;&#x2122;s Multilingual Neural Machine Translation System. Our readers includes a group of people who have shown passionate interest in computational linguistics and related fields. They have continuously empowered and criticized all our endeavours and it has served as a motive force for the entire CLEAR team. On this hopeful prospect, I proudly present this edition of CLEAR to our faithful readers and look forward to your opinions and criticism. Best Regards, Shine S (Chief Editor)

CLEAR SEPTEMBER 2017

Common sense reasoning Nayana R M M.Tech Computational Linguistics Government Engineering College, Sreekrishnapuram nayanarm03@gmail.com

Commonsense reasoning is one of the branches of artificial intelligence (AI) that is concerned with simulating the human ability to make presumptions about the type and essence of ordinary situations they encounter every day. These assumptions include judgments about the physical properties, purpose, intentions and behavior of people and objects, as well as possible outcomes of their actions and interactions. A device that exhibits commonsense reasoning will be capable of predicting results and drawing conclusions that are similar to humans' folk psychology (humans' innate ability to reason about people's behavior and intentions) and naĂŻve physics (humans' natural understanding of the physical world). In spite of the recent shift of interest of the computational linguistics community to statistical approaches to NLP, the role of commonsense reasoning in semantic and pragmatic processing remains strong. Traditionally, it has been the commonsense reasoning community, and, in particular, the no monotonic CLEAR SEPTEMBER 2017

reasoning community, that have considered linguistic applications of reasoning; however, these researchers do not go all the way to demonstrate how their formalisms can be implemented as algorithms that would enhance the quality of a particular NLP system. In Artificial intelligence, commonsense knowledge is the set of background information that an individual is intended to know or assume and the ability to use it when appropriate. It is a shared knowledge (between everybody or people in a particular culture or age group only). The way to obtain commonsense is by learning it or experiencing it. In communication, it is what people donâ&#x20AC;&#x2122;t have to say because the interlocutor is expected to know or make a presumption about. The commonsense knowledge problem is a current project in the sphere of artificial intelligence to create a database that contains the general knowledge most individuals are expected to have, represented in an accessible way to artificial intelligence programs that use natural 5

language. Due to the broad scope of the commonsense knowledge this issue is considered to be among the most difficult ones in the AI research sphere. In order for any task to be done as a human mind would manage it, the machine is required to appear as intelligent as a human being. Such tasks include object recognition, machine translation and text mining. To perform them, the machine has to be aware of the same concepts that an individual, who possess commonsense knowledge, recognizes. Regrettably, most commercial search systems do not rely on commonsense reasoning, and as a result it is difficult to access a relevant piece of information if the user is not familiar with domain-specific terms. Furthermore, it is not yet possible to find information on the World Wide Web that is related to a query only by common sense Significant progress in the field of the automated commonsense reasoning is made in the areas of the taxonomic reasoning, actions and change reasoning, reasoning about time. Each of these spheres has a well-acknowledged theory for wide range of commonsense inferences. Common senseâ&#x20AC;&#x2122;s reasoning study is divided into knowledge-based approaches and approaches that are based on machine learning over and using a large data corpora with limited CLEAR SEPTEMBER 2017

interactions between these two types of approaches. There are also crowdsourcing approaches, attempting to construct a knowledge basis by linking the collective knowledge and the input of nonexpert people. Knowledge-based approaches can be separated into approaches based on mathematical logic. In knowledge based approaches, the experts are analyzing the characteristics of the inferences that are required to do reasoning in a specific area or for a certain task. The knowledge-based approaches consist of mathematically grounded approaches, informal knowledgebased approaches and large-scale approaches. The mathematically grounded approaches are purely theoretical and the result is a printed paper instead of a program. The work is limited to the range of the domains and the reasoning techniques that are being reflected on. In informal knowledge-based approaches, theories of reasoning are based on anecdotal data and intuition that are results from empirical behavioral psychology. Informal approaches are common in computer programming. Two other popular techniques for extracting commonsense knowledge from Web 6

documents involve Web mining and Crowd sourcing.

represented in a form that is usable by computers.

Challenges

Third, commonsense reasoning involves plausible reasoning. It requires coming to a reasonable conclusion given what is already known. Plausible reasoning has been studied for many years and there are a lot of theories developed that include probabilistic reasoning and nonmonotonic logic. It takes different forms that include using unreliable data and rules, whose conclusions are not certain sometimes.

As of 2014, there are some commercial systems trying to make the use of commonsense reasoning significant. However, they use statistical information as a proxy for commonsense knowledge, where reasoning is absent. Current programs manipulate individual words, but they donâ&#x20AC;&#x2122;t attempt or offer further understanding. Five major obstacles interfere with the producing of a satisfactory "commonsense reasoner". First, some of the domains that are involved in commonsense reasoning are only partly understood. Individuals are far from a comprehensive understanding of domains as communication and knowledge, interpersonal interactions or physical processes. Second, situations that seem easily predicted or assumed about could have logical complexity, which humansâ&#x20AC;&#x2122; commonsense knowledge does not cover. Some aspects of similar situations are studied and are well understood, but there are many relations that are unknown, even in principle and how they could be CLEAR SEPTEMBER 2017

Fourth, there are many domains, in which a small number of examples are extremely frequent, whereas there is a vast number of highly infrequent examples. Fifth, when formulating preassumptions it is challenging to discern and determine the level of abstraction. Approaches and techniques Common sense reasoning study is divided into knowledge-based approaches and approaches that are based on machine learning over and using a large data corpora with limited interactions between these two types of approaches. There are also 7

crowdsourcing approaches, attempting to construct a knowledge basis by linking the collective knowledge and the input of nonexpert people. Knowledge-based approaches can be separated into approaches based on mathematical logic. In knowledge-based approaches, the experts are analyzing the characteristics of the inferences that are required to do reasoning in a specific area or for a certain task. The knowledge-based approaches consist of mathematically grounded approaches, informal knowledgebased approaches and large-scale approaches. The mathematically

grounded approaches are purely theoretical and the result is a printed paper instead of a program. The work is limited to the range of the domains and the reasoning techniques that are being reflected on. In informal knowledge-based approaches, theories of reasoning are based on anecdotal data and intuition that are results from empirical behavioral psychology. Informal approaches are common in computer programming. Two other popular techniques for extracting commonsense knowledge from Web documents involve Web mining and Crowd sourcing.

Intelâ&#x20AC;&#x2122;s Loihi Intel introduced a first-of-a-kind self-learning chip that mimic how the human brain works. According to Intel, Loihi is an extremely energy-efficient chip that uses data to learn and make inferences. It gets smarter over time and doesn't need to be trained. It takes a novel approach to computing via asynchronous spiking. Loihi learns to operate based on various modes of feedback it receives from the environment. It is based on the concept of neuromorphic computing, which itself is derived from scientists' understanding of how the brain works. Combined with its ability to learn, Intel says Loihi paves the way for machines to be autonomous and to adapt in real-time instead of waiting for updates from the cloud. Intel is planning to share its Loihi test chip with leading universities and research facilities in the first half of 2018, with a focus on advancing AI.

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Human Computer Dialogue Systems Jenny George M.Tech Computational Linguistics Government Engineering College, Sreekrishnapuram Jennygeorge763@gmail.com

ABSTRACT: The term “dialogue” is used in different communities in different ways. Many researchers in the speech recognition community view “dialogue methods” as a way of controlling and restricting the interaction. The evaluation and development of spoken dialogue systems is a complex undertaking, and much effort is expended on making it manageable. Research and industry endeavours in the area often seek to compare versions of existing systems, or to compare component technologies, in order to find the best methods where “best” is defined as most efficient. This paper studies three approaches of human computer dialogue systems. One possibility is to assess whether they are based on the agent intention or on social conventions. An important view of dialogue involves basing human-computer interaction on human conversation. In CLEAR SEPTEMBER 2017

this view, dialogue enhances the richness of the interaction and allows more complex information to be conveyed than is possible in a single utterance. Understanding what a computer is doing in human terms rather than in computer terms was an early step in human-computer interaction. As humans observed what computers could do, they adapted the computer’s capabilities to satisfy their needs and desires. Humans and computers use data and information differently. These differences have two aspects: the form of the data or information and the processing applied to the data or information. Computer-based content has to be presented to humans in forms that are different from internal computer form, and humans process that information in different ways than do computers. By understanding the functional capabilities and limitations of computing systems and of humans, we can create an on-line world that 9

best serves human needs. One can view the context as being represented as a set of parameters that need to be before the system action can be taken instantiated. For example, to provide information about train arrivals and departures, the system needs to know parameters like the train id number, the event involved (e.g., arriving or departing), the day of travel, and so on. The action is performed as soon as enough information has been identified. This approach has been used for systems providing information about current movies, information about train schedules, and for describing routes to restaurants.

Fig: A short example dialogue Spoken dialogue system research is often guided by a wish to achieve more natural interaction. The term “natural” is somewhat problematic and rarely defined, but is generally taken to mean something like “more like human-human interaction”. For example, Jokinen (2003) talks about “computers that mimic human CLEAR SEPTEMBER 2017

interaction” and Boyce (2000) says “the act of using natural speech as input mechanism makes the computer seem more human-like”. We will use “human-like” to mean “more like human-human interaction”. Interfaces and Interlocutors The thought that users interpret spoken dialogue systems metaphorically is not far-fetched: metaphors help us understand something that is odd to us in terms of something that is not. Talking computers are not yet commonplace, so interpreting them in terms of something that is more familiar to us makes sense. It is worth noting that metaphors may be seen from a designer as well as a user perspective. In the first case, the metaphor is what the designer intends for the user to perceive. In the second case, it is the image in the light of which the user actually understands the system. Within the interface metaphor, the spoken dialogue system is perceived as a machine interface often a computer interface. Speech is used to accomplish what would have otherwise been accomplished by some other means of input, such as a keyboard or a mouse. If a user perceiving a train ticket booking 10

system in this manner says “Bath”, it is equivalent to choosing Bath in for example a web form. The interface metaphor lies close at hand because many of the spoken dialogue systems that users are in contact with today basically provide alternatives to existing interfaces. Within the human metaphor, on the other hand, the computer is perceived as an interlocutor: a being with human-like conversational abilities. Speech is not a substitute, nor is it one of many alternatives – it is the primary means of communication. The process of evaluating isolated features is sometimes called micro-evaluation. The evaluation of systems as a whole is called macro-evaluation. The steps introduced below are all micro evaluation scheme. Developing and evaluating a component for increased human-likeness can be described as a multi-step process. Candidate selection: The first step, then, is to identify candidate features for the sought after effect. This can be done by studying the literature if the phenomenon has been researched, or by exploratory data studies if it has not. In order to find candidates for human-likeness, it is generally best to CLEAR SEPTEMBER 2017

use human-human dialogue. The selected candidate can then be tested for perception, understanding and response. Perception: The easiest tests to perform are simple perception tests to see if subjects can perceive the phenomenon. Understanding: Once it is established that a feature can be perceived and that a distinction can be made, experiments to find out how it is perceived are needed. The fact that a listener is able to perceive the difference between two stimuli does not prove that the difference means anything in particular. Response: When it is shown that users can perceive and understand a feature in the way it was intended, it is time to test the pragmatics, thus finding out whether subjects respond accordingly something which standard listening or production test are not likely to achieve. Major Challenges  The pattern - matching techniques used to great effect in frame-based and sequentialcontext systems simply do not work for more complex 11

domains. They do not capture enough of the subtlety and distinctions that people depend on in using language. We need to produce a detailed semantic (i.e., “deep”) representation of what was said–something that captures what the user meant by the utterance.  The second problem is how to build a dialogue system that can be adapted easily to most any practical task. Given the range of applications that might be used, from information retrieval, to design, to emergency relief management, to tutoring, we cannot place very strong constraints on what the application program looks like.  Human practical dialogue involves mixed-initiative interaction, i.e., it involves the dynamic exchange of control of dialogue flow, increasing dialogue effectiveness and efficiency and enabling both participant’s needs to be met. In contrast, in fixed initiative dialogue one participant controls the interaction throughout. It does not work CLEAR SEPTEMBER 2017

well when you need to do something a little out of the normal path. Intention Based approach and Convention Based approaches Intention based approaches use a representation of the mental states of the artificial agent. The most famous model is BDI (Belief, Desire and Intention). which has been used both in logic (Cohen and Levesque, 1990) and planning (Allen and Perrault, 1980) settings. Its implementation is complex and its reuse is domain restricted. To simplify, a dialog can be considered as a protocol represented by finite state automata in which transitions are the possible speech acts of the dialog in convention approach. The agent has no internal representation. These approaches are rather rigid even if some of them (Sitter and Stein, 1992) use recursive automata. Another conventional model (Lewis, 1979) consists in representing information shared during the dialog (called “common ground”) in a conversational board. This theory is more descriptive than predictive and thus is difficult to integrate into a dialog system.

Mixed Approaches: Dialog games (Levin and Moore, 1980) are interested in social conventions between utterances. They use structures, games for which interactions are precisely described. Games are stereotypes that model a communicational situation. A Natural Dialogue System is a form of dialogue system that tries to improve usability and user satisfaction by imitating human behaviour. It addresses the features of

a human-to-human dialog (e.g. sub dialogues and topic changes) and aims to integrate them into dialog systems for human machine interaction. Often, (spoken) dialog systems require the user to adapt to the system because the system is only able to understand a very limited vocabulary, is not able to react on topic changes, and does not allow the user to influence the dialogue flow.

Cognitive computing Vs Artificial Intelligence Artificial intelligence agents decide which actions are the most appropriate to take, and when they should be taken. These agents most often take the form of machine learning algorithms, neural networks, statistical analysis and more. We feed the AI system with information over a long period of time so that it can “learn” the variables it should pay attention to and the desired outcomes and it finally spits out a solution. Cognitive computing is often described as simply marketing jargon, so crafting a working definition is important, although it’s more fluid right now, and there isn’t one consensus that industry experts have settled on. Still, the foundation is that cognitive computing systems try to simulate human thought processes. This process uses many of the same fundamentals as AI, such as machine learning, neural networks, natural language processing, contextual awareness and sentiment analysis, to follow the problemsolving processes that humans do day in and day out. Main difference is that artificial intelligence does not try to mimic human thought processes like cognitive computing systems. Instead, a good AI system is the simply the best possible algorithms for solving a given problem. Similarly cognitive computing does not make decisions for humans like AI systems, but rather supplements our own decision-making.

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Lexical Simplification Silpa K S M.Tech Computational Linguistics Government Engineering College, Sreekrishnapuram Silpasasidharan75@gmail.com

Lexical simplification (LS) is the process of replacing complex words with simpler alternatives. This is a challenging task since the substitutions must preserve both the original meaning and the grammaticality of the sentence being simplified. LS contain the following steps: 1. Complex Word Identification (CWI) to select words to simplify; 2. Substitution Generation (SG) to produce candidate substitutions for each complex word; 3. Substitution Selection (SS) to filter candidates that do not fit the context of the complex word; and 4. Substitution Ranking (SR) to rank them according to their simplicity. These steps are illustrated in Figure 1. The first stage in any lexical simplification system is complex word identification (CWI). There are 2 main approaches to this. Firstly, systems will attempt to CLEAR SEPTEMBER 2017

simplify every word and those for which simplifications can be found are transformed, those which cannot be transformed are left. Secondly, some form of thresholding is applied. There are various measures of lexical complexity - which often reside heavily in word frequency. Some threshold may be applied to one of these measures to determine between complex and simple words.

Substitution Generation (SG) is the task of producing candidate substitutions for complex words, which is normally done regardless of the context of the complex word. We can use different technique for generating substitutions. One of the simplest technique is to extract candidates from a parallel Wikipedia and Simple Wikipedia corpus. It requires a set of tagged parallel sentences and the word alignments between them in Pharaoh format. Another generator called Biran Generator filters substitutions based on the Cartesian product between vocabularies of complex and simple words. It requires vocabularies of complex and simple words, as well as two language models trained over complex and simple corpora. WordNet also helped for SG for extracting synonyms. The usage of word embedding models for SG, leading to even better results. In this a context-aware word embeddings model trained over a corpus composed of words concatenated with universal POS tags. Substitution Selection (SS) is the task of selecting which substitutions – from a given list – can replace a complex word in a given sentence without altering its meaning. CLEAR SEPTEMBER 2017

Most work addresses this task referring to the context of the complex word by employing Word Sense Disambiguation (WSD) approaches or by discarding substitutions which do not share the same POS tag of the target complex word. Biran selector and Word vector selector are also used for SS. Biran Selector employs a strategy in which a word cooccurrence model is used to determine which substitutions have meaning similar to that of a target complex word. It requires a plain text file with each line in the format specified in Example 1, where <wi> is a word, <cij> a co-occurring word and <fij> its frequency of occurrence. <wi> <ci0> : <fi0> ... <cin> : <fin> (1) Each component in the format in 1 must be separated by a tabulation marker. Given such a model, the approach filters all substitutions which are estimated to be more complex than the target word, and also those for which the distance between their co-occurrence vector and the target sentence’s vector is higher than a threshold set by the user. In WordVectorSelector, it employs a novel strategy, in which a 15

word vector model is used to determine which substitutions have the closest meaning that of the sentence being simplified. It requires a binary word vector model produced by Word2Vec , and can be configured in many ways. It retrieves a userdefined percentage of the substitutions, which are ranked with respect to the cosine distance between their word vector and the sum of some or all of the sentencesâ&#x20AC;&#x2122; words, depending on the settings defined by the user. Substitution Ranking (SR) is the task of ranking a set of selected substitutions for a target complex word with respect to their simplicity. Approaches vary from simple word length and frequency based measures to more sophisticated linear combinations of scoring functions as well as machine learning-based approaches. Metric Ranker, SVM Ranker, Boundary Ranker and Neural Ranker are some of the ranking strategies used for this task. Metric ranking is based on the values of a single feature provided by the user. By configuring the input FeatureEstimator object, the user can calculate values of several features for the candidates in a given CLEAR SEPTEMBER 2017

dataset and easily rank the candidates according to each of these features. Metric ranking is based on the values of a single feature provided by the user. By configuring the input FeatureEstimator object, the user can calculate values of several features for the candidates in a given dataset and easily rank the candidates according to each of these features. SVMRanker use Support Vector Machines in a setup that minimises a loss function with respect to a ranking model. The user needs to provide a path to their SVM Rank installation, as well as SVM related configurations, such as the kernel type and parameter values for C, epsilon, etc. BoundaryRanker Employs a novel strategy, in which ranking is framed as a binary classification task. During training, this approach assigns the label 1 to all candidates of rank 1 â&#x2030;Ľ r â&#x2030;Ľ p, where p is a range set by the user, and 0 to the remaining candidates. It then trains a stochastic descent linear classifier based on the features specified in the Feature Estimator object. During testing, candidate substitutions are ranked based on how far from 0 they are. This ranker allows the user to provide several parameters during 16

training, such as loss function and penalty type. Neural ranking includes three steps: Regression, Ordering and Confidence Check

candidate, we first compare the use of this candidate against the original word in context, which can be seen as a Confidence Check.

Figure 2: Architecture of Neural Ranker

At the end of substitution Ranking we get most suited word in the top most position. Now the task is to replace the complex word with that simpler word. Lexical simplification can be used to simplify the scientific documents, thereby it become more accessible to general audiences. It also used in several documents to reduce the complexity of sentence.

In regression, a multi-layer perceptron is used to determine the ranking between candidate substitutions. The network (Figure 2) takes as input a set of features from two candidates, and produces a single value that represents how much simpler candidate 1 is than candidate 2. If the value is negative, then candidate 1 is simpler than 2, if it is positive, candidate 2 is simpler than 1. In the step ordering some scores are given to the candidate words based on predefined formulas and rank them according to the scores. Once candidates are ranked, in order to increase the reliability of our simplifier, instead of replacing the target complex word with the simplest CLEAR SEPTEMBER 2017

NLP in Google Cloud Search

Google has added natural language processing to G Suiteâ&#x20AC;&#x2122;s Cloud Search function, allowing G Suite customers to search through workplace files, documents. Cloud Search can now accept relative, natural commands, which means that you can look for documents shared by a certain person, documents that need your attention, presentations shared on a certain day, schedule items of a certain type, and other broad queries. Google is also applying its machine learning powers to answer queries like â&#x20AC;&#x153;What docs need my attention?â&#x20AC;?. Besides finding documents, Cloud Search can also answer questions about people, much like the Knowledge Graph in regular Google Search. 17

Sort Story Shabna Nasser1, Fathima Shabana K2, Fathima Shirin A3, Fathima Riswana K4 M.Tech Computational Linguistics Government Engineering College, Sreekrishnapuram shabnanasser@gmail.com1, fathimamkd1993@gmail.com2, fathimashirin94@gmail.com3, fathimariswana024@gmail.com4

Sort Story refers to the task of sequencing. A jumbled set of aligned image-caption pairs that belong to a story, is sorted such that the output sequence forms a coherent story. There are multiple approaches to perform these tasks, which include unary (position) and pair wise (order) predictions, and their ensemble-based combinations for achieving strong results on this task. Also, various textbased and image-based features can be used for complementary improvements. Sequencing is a task for children that is aimed at improving understanding of the temporal occurrence of a sequence of events. The task is, given a jumbled set of images (and maybe captions) that belong to a single story, sort them into the correct order so that they form a coherent story. This work is to enable AI systems to better understand and predict the temporal nature of events in the world. CLEAR SEPTEMBER 2017

Temporal reasoning has a number of applications such as multidocument summarization of multiple sources like, news information where the relative order of events can be useful to accurately merge information in a temporally consistent manner. The major contributions to carry out the process of sorting a jumbled set of image caption pairs involves, (1) the task of visual story sequencing. (2) the two approaches to solve the task: one based on individual story elements to predict position, and the other based on pair wise story elements to predict relative order of story elements. Also, then combine these approaches in a voting scheme that outperforms the individual methods. As features, a story element can be represented as both text-based features from the caption and image-based features, and can show that they provide complementary improvements. For text-based features, both sentence context and relative order based 18

distributed representations can be used.

models, so that the output sequence forms a coherent story.

Augmenting the text features with image features results in a visible performance improvement of both the model trained with unary features and the model trained with pairwise features. While image features by themselves result in poor performance on this task, they seem to capture temporal information that is complementary to the text features.

We can use deep neural networks for solving unary model approaches, can use two neural networks. (1) A language-alone unary model (Skip-Thought + MLP) that uses a Gated Recurrent Unit (GRU) to embed a caption into a vector space. These embeddings are fed as input into a Multi-Layer Perceptron (MLP). (2) A language + vision unary model (SkipThought + CNN+MLP) that embeds the caption and embeds the image via a Convolutional Neural Network (CNN).

Figure 1: (a) The input is a jumbled set of image-caption pairs. (b) Actual output of the system â&#x20AC;&#x201C; an ordered sequence of image-caption pairs that form a coherent story. Figure 1 depicts that a set of jumbled images and their corresponding captions, which is taken from a sequential image narrative dataset, which is given as input, after using various unary and pairwise models and train machine learning

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In pairwise modeling approach we can use 3 models. (1) A languagealone pairwise model (Skip- Thought + MLP) that takes as input a pair of Skip-Thought embeddings and trains an MLP. (2) A language + vision pairwise model (Skip- Thought + CNN + MLP) that concatenates the Skip- Thought and CNN embeddings and trains a similar MLP. (3) A language-alone neural position embedding (NPE) model. Instead of using frozen Skip-Thought embeddings, learns a task-aware ordered distributed embedding for sentences. Specifically, each sentence in the story is embedded via an LSTM.

To combine the complementary information captured by the unary and pairwise models, can use a votingbased ensemble. For each method in the ensemble, we find the top three permutations. Each of these permutations then vote for a particular element to be placed at a particular position. The combination of pairwise Skip-Thought+CNN+MLP and neural position embeddings have proven to work well. These approaches train and evaluate the model on personal multimodal stories from the SIND (Sequential Image Narrative Dataset) where each story is a sequence of 5 images and corresponding story-like captions. The narrative captions in this dataset, e.g., “friends having a good time” (as opposed to “people sitting next to each other”) capture a sequential, conversational language, which is characteristic of stories. Uses about 40,155 stories for training, 4990 for validation and 5055 stories for testing. The evaluation of performance of these models at correctly ordering a jumbled set of story elements using the following 3 metrics: Spearman’s rank correlation measures if the ranking of story elements in the predicted and ground truth orders are monotonically related (higher is better). Pairwise accuracy measures the fraction of pairs of elements whose predicted relative ordering is the same CLEAR SEPTEMBER 2017

as the ground truth order (higher is better). Average Distance, measures the average change in position of all elements in the predicted story from their respective positions in the ground truth story. It has been proven through several experiments that the pairwise models based on Skip-Thought features outperform the unary models in this task. However, the Pairwise Order Model performs worse than the unary Skip-Thought model, suggesting that the Skip-Thought features, which encode context of a sentence, also provide a crucial signal for temporal ordering of story sentences. References: [1] Nathanael Chambers and Daniel Jurafsky. 2008. Unsupervised learning of narrative event chains. In ACL. Citeseer. [2] Nasrin Mostafazadeh Ishan Misra Aishwarya Agrawal Jacob Devlin Ross Girshick Xiaodong He Pushmeet Kohli Dhruv Batra C. Lawrence Zitnick Devi Parikh Lucy Vanderwende Michel Galley Margaret Mitchell Ting-Hao Huang, Francis Ferraro. 2016. Visual storytelling. In NAACL. [3] Sepp Hochreiter and J¨urgen Schmidhuber. 1997. Long short-term memory. Neural computation.

M.Tech Computational Linguistics Dept. of Computer Science and Engg, Govt. Engg. College, Sreekrishnapuram Palakkad www.simplegroups.in simplequest.in@gmail.com

SIMPLE Groups Students Innovations in Morphology Phonology and Language Engineering

Article Invitation for CLEAR- December-2017 We are inviting thought-provoking articles, interesting dialogues and healthy debates on multifaceted aspects of Computational Linguistics, for the forthcoming issue of CLEAR (Computational Linguistics in Engineering And Research) Journal, publishing on December 2017. The suggested areas of discussion are:

The articles may be sent to the Editor on or before 10th December, 2017 through the email simplequest.in@gmail.com. For more details visit: www.simplegroups.in Editor,

Representative,

CLEAR Journal

SIMPLE Groups

CLEAR SEPTEMBER 2017

Hello world, Common Sense Reasoning enables a computer system to make judgments and predic outcomes about various situations that happen in the day to day life. Various knowledge based approach and machine learning based approaches are proposed to construct such systems. Dialogue or Speech is a major medium used for human-computer interaction. Increase in the use of smart phones demands effective systems that can handle input in the form of dialogue. Replacing complex words with simpler ones helps to understand the content better and this is done by lexical simplification systems. Sorting systems helps to do the sequencing tasks like sorting jumbled story lines easily. This issue of CLEAR Journal contains articles about some interesting topics like Common sense reasoning, Human Computer Dialogue Systems, Lexical Simplification and Sorting of Jumbled Story lines.. The articles are written with the hope of spreading some light to the various trending fields related to computational linguistics. CLEAR is thankful to all who have given their time and effort for introducing their valuable ideas and suggestions. Simple group invites more strivers in this field. Wish you all the success in your future endeavorsâ&#x20AC;Ś!!!

Sreelakshmi K

CLEAR SEPTEMBER 2017