Abstract

While extractive summarization is a well studied problem, it is far from solved. In recent years a large number of interesting and complex models have been used to achieve significant improvements in performance. This can easily be attributed to Deep Learning models and dense vector representations but the performance gain comes with the cost of computational and representational complexity. In this work, we present a simple, yet effective approach for extractive summarization of news articles. In line with many recent works in this area we propose an encoder-decoder architecture with a simple bag of word encoder for sentences followed by an attention based decoder for relevant sentence selection. Our model is trained end-to-end and its performance is comparable to the state-of-the-art models while being simpler both in terms of the number of parameters (significantly lesser) as well as the representational complexity.

Abstract

In this paper, we model the similar question retrieval task as a binary classification problem. We propose a novel approach of “1D-Siamese LSTM for cQA (1D-SLcQA)” to find the semantic similarity between a new question and existing question(s). In 1D-SLcQA, we use a combination of twin LSTM networks and a contrastive loss function to effectively memorize the long term dependencies i.e., capture semantic similarity even when the length of the answers/questions is very large (200 words). The similarity of the questions is modeled using a single network with (1D) (feature) convolution between feature vectors learned from twin LSTM layers. Experiments on large scale real world Yahoo Answers dataset show that 1D-SLcQA outperform the state of the art approach of Siamese cQA approach (SCQA).

Abstract

n the past few years, bully and aggressive posts on social media have grown signifi-cantly, causing serious consequences for vic-tims/users of all demographics. Majority of the work in this field has been done for En-glish only. In this paper, we introduce a deep learning based classification system for Face-book posts and comments of Hindi-English Code-Mixed text to detect the aggressive be-haviour of/towards users. Our work focuses ontext from users majorly in the Indian Subcon-tinent. The dataset that we used for our mod-els is provided byTRAC-11in their shared task. Our classification model assigns each Facebook post/comment to one of the threepredefined categories: “Overtly Aggressive”,“Covertly Aggressive” and “Non-Aggressive”.We experimented with 6 classification mod-els and our CNN model on a 10 K-fold cross-validation gave the best result with the predic-tion accuracy of 73.2%

Abstract

Extractive summarization methods operate by ranking and selecting the sentences which best encapsulate the theme of a given document. They do not fare wellin domains like fictional narratives where there is no central theme and core in-formation is not encapsulated by a small set of sentences. For the purpose ofreducing the size of the document while conveying the idea expressed by each sentence, we need more sentence specific methods. Telegraphic summarization,which selects short segments across several sentences, is better suited for such domains. Telegraphic summarization captures the plot better by retaining shorter versions of each sentence while not really concerning itself with grammatically linking these segments. In this paper, we propose an unsupervised deep learning network (NUTS) to generate telegraphic summaries. We use multiple encoder-decoder networks and learn to drop portions of the text that are inferable from thechosen segments. The model is agnostic to both sentence length and style. We demonstrate that the summaries produced by our model show significant quanti-tative and qualitative improvement over those produced by existing methods and baselines.

Abstract

A key part of software maintenance is bug reporting and rectifica-tion. Bug reporting is a major issue and due to its asynchronous nature, duplicate bug reporting is common. Detecting duplicate bug reports is an important task in software maintenance in order to avoid the assignment of the same bug to different developers.In this paper, we explore the notion of using word embeddings for retrieving duplicate bug report in large software repositories. We discuss an approach to model each bug report as a dense vector and retrieve its top-k most similar reports for duplicate bug report de-tection. Through experiments on two real world datasets, we show that word embeddings perform better than baselines and related approaches and have the potential to improve duplicate bug report retrieval.

Abstract

In this paper, we leverage social media platforms such as twitter for developing corpus across multiple languages. The corpus creation methodology is applicable for resource-scarce languages provided the speakers of that particular language are active users onsocial media platforms. We present an approach to extract social media microblogs suchas tweets (Twitter). In this paper, we create corpus for multilingual sentiment analysis and emoji prediction in Hindi, Bengali and Telugu. Further, we perform and analyze multiple NLP tasks utilizing the corpus to get interesting observations

Abstract

Most extractive summarization techniques operate by ranking all the source sentences and then select the top-ranked sentences as the summary. Such methods are known to produce good sum-maries, especially when applied to news articles and scientific texts. However, they do not fareso well when applied to texts such as fictional narratives, which do not have a single central or recurrent theme. This is because usually the information or plot of the story is spread across sev-eral sentences. In this paper, we discuss a different summarization technique called Telegraphic Summarization. Here, we do not select whole sentences, rather pick short segments of text spread across sentences, as the summary. We have tailored a set of guidelines to create such summaries and, using the same, annotate a gold corpus of 200 English short stories.

Abstract

Code-mixing, use of two or more languages in a single sentence, is ubiquitous; generated by multi-lingual speakers across the world. The phenomenon presents itself prominently in social media discourse. Consequently, there is a growing need for translating code-mixed hybrid language into standard languages. However, due to the lack of gold parallel data, existing machine translation systems fail to properly translate code-mixed text. In an effort to initiate the task of machine translation of code-mixed content, we present a newly created parallel corpus of code-mixed English-Hindi and English. We selected previously available English-Hindi code-mixed data as a starting point for the creation of our parallel corpus. We then chose 4 human translators, fluent in both English and Hindi, for translating the 6088 code-mixed English-Hindi sentences to English. With the help of the created parallel corpus, we analyzed the structure of English-Hindi code-mixed data and present a technique to augment run-of-the-mill machine translation (MT) approaches that can help achieve superior translations without the need for specially designed translation systems. We present an augmentation pipeline for existing MT approaches, like Phrase Based MT (Moses) and Neural MT, to improve the translation of code-mixed text. The augmentation pipeline is presented as a pre-processing step and can be plugged with any existing MT system, which we demonstrate by improving translations done by systems like Moses, Google Neural Machine Translation System (NMTS) and Bing Translator for English-Hindi code-mixed content.

Abstract

In this paper, we describe Transzaar -an AI powered tool that offers computer aided translation (CAT) functionality: pre-translation analysis,post-editing machine translated content, translation prediction, text aligning, extensive logging and integration with several machine translation (MT) systems. Transzaar aids a human translator to perform various language processing tasks, viz., Translation, Transliteration, Localization, and other kinds of Text Analysis tasks. Using Transzaar, human translators can post-edit the machine translated content, to improve fluency and accuracy of the translated content to match the naturalness of human translation while delivering with better turn-around time.Transzaar aids the process of post-editing, thereby increasing the productivity of human translators by 2-3 folds within couple of months of usage for certain language pairs. It collects feedback continuously, which helps the MT system to further learn and improve periodically, with the additional new generated data-set

Abstract

Named Entity Recognition (NER) is a major task in the field of Natural Language Processing (NLP), and also is a sub-task of Information Extraction. The challenge of NER for tweets lie in the insufficient information available in a tweet. There has been a significant amount of work done related to entity extraction, but only for resource rich languages and domains such as newswire. Entity extraction is, in general, a challenging task for such an informal text, and code-mixed text further complicates the process with it’s unstructured and incomplete information. We propose experiments with different machine learning classification algorithms with word, character and lexical features. The algorithms we experimented with are Decision tree, Long Short-Term Memory (LSTM), and Conditional Random Field (CRF). In this paper, we present a corpus for NER in Hindi-English Code-Mixed along with extensive experiments on our machine learning models which achieved the best f1-score of 0.95 with both CRF and LSTM.