pwc-archive/papers-with-abstracts huggingface dataset:
Towards Opinion Summarization of Customer Reviews
Authors: Samuel Pecar
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Abstractive Text Summarization, Decision Making, Opinion Summarization, Text Summarization

In recent years, the number of texts has grown rapidly. For example, most review-based portals, like Yelp or Amazon, contain thousands of user-generated reviews. It is impossible for any human reader to process even the most relevant of these documents. The most promising tool to solve this task is a text summarization. Most existing approaches, however, work on small, homogeneous, English datasets, and do not account to multi-linguality, opinion shift, and domain effects. In this paper, we introduce our research plan to use neural networks on user-generated travel reviews to generate summaries that take into account shifting opinions over time. We outline future directions in summarization to address all of these issues. By resolving the existing problems, we will make it easier for users of review-sites to make more informed decisions.
Sampling Informative Training Data for RNN Language Models
Authors: Fern, Jared ez, Doug Downey
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Language Modeling, Language Modelling

We propose an unsupervised importance sampling approach to selecting training data for recurrent neural network (RNNs) language models. To increase the information content of the training set, our approach preferentially samples high perplexity sentences, as determined by an easily queryable n-gram language model. We experimentally evaluate the heldout perplexity of models trained with our various importance sampling distributions. We show that language models trained on data sampled using our proposed approach outperform models trained over randomly sampled subsets of both the Billion Word (Chelba et al., 2014 Wikitext-103 benchmark corpora (Merity et al., 2016).
Learning-based Composite Metrics for Improved Caption Evaluation
Authors: Naeha Sharif, Lyndon White, Mohammed Bennamoun, Syed Afaq Ali Shah
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Image Captioning, Language Modeling, Language Modelling, Semantic Textual Similarity, Sentence

The evaluation of image caption quality is a challenging task, which requires the assessment of two main aspects in a caption: adequacy and fluency. These quality aspects can be judged using a combination of several linguistic features. However, most of the current image captioning metrics focus only on specific linguistic facets, such as the lexical or semantic, and fail to meet a satisfactory level of correlation with human judgements at the sentence-level. We propose a learning-based framework to incorporate the scores of a set of lexical and semantic metrics as features, to capture the adequacy and fluency of captions at different linguistic levels. Our experimental results demonstrate that composite metrics draw upon the strengths of stand-alone measures to yield improved correlation and accuracy.
Pushing the Limits of Radiology with Joint Modeling of Visual and Textual Information
Authors: Sonit Singh
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Image Classification, Machine Translation, Object Detection, Question Answering, Retrieval, Semantic Segmentation, Visual Dialog, Visual Question Answering, Visual Question Answering (VQA)

Recently, there has been increasing interest in the intersection of computer vision and natural language processing. Researchers have studied several interesting tasks, including generating text descriptions from images and videos and language embedding of images. More recent work has further extended the scope of this area to combine videos and language, learning to solve non-visual tasks using visual cues, visual question answering, and visual dialog. Despite a large body of research on the intersection of vision-language technology, its adaption to the medical domain is not fully explored. To address this research gap, we aim to develop machine learning models that can reason jointly on medical images and clinical text for advanced search, retrieval, annotation and description of medical images.
Recognizing Complex Entity Mentions: A Review and Future Directions
Authors: Xiang Dai
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Entity Linking, Named Entity Recognition (NER), Question Answering, Relation Extraction, Sentence

Standard named entity recognizers can effectively recognize entity mentions that consist of contiguous tokens and do not overlap with each other. However, in practice, there are many domains, such as the biomedical domain, in which there are nested, overlapping, and discontinuous entity mentions. These complex mentions cannot be directly recognized by conventional sequence tagging models because they may break the assumptions based on which sequence tagging techniques are built. We review the existing methods which are revised to tackle complex entity mentions and categorize them as tokenlevel and sentence-level approaches. We then identify the research gap, and discuss some directions that we are exploring.
Automatic Detection of Cross-Disciplinary Knowledge Associations
Authors: Menasha Thilakaratne, Katrina Falkner, Thushari Atapattu
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7

Detecting interesting, cross-disciplinary knowledge associations hidden in scientific publications can greatly assist scientists to formulate and validate scientifically sensible novel research hypotheses. This will also introduce new areas of research that can be successfully linked with their research discipline. Currently, this process is mostly performed manually by exploring the scientific publications, requiring a substantial amount of time and effort. Due to the exponential growth of scientific literature, it has become almost impossible for a scientist to keep track of all research advances. As a result, scientists tend to deal with fragments of the literature according to their specialisation. Consequently, important and hidden associations among these fragmented knowledge that can be linked to produce significant scientific discoveries remain unnoticed. This doctoral work aims to develop a novel knowledge discovery approach that suggests most promising research pathways by analysing the existing scientific literature.
Language Identification and Named Entity Recognition in Hinglish Code Mixed Tweets
Authors: Kushagra Singh, Indira Sen, Ponnurangam Kumaraguru
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Abuse Detection, Chunking, Language Identification, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, POS, Semantic Role Labeling

While growing code-mixed content on Online Social Networks(OSN) provides a fertile ground for studying various aspects of code-mixing, the lack of automated text analysis tools render such studies challenging. To meet this challenge, a family of tools for analyzing code-mixed data such as language identifiers, parts-of-speech (POS) taggers, chunkers have been developed. Named Entity Recognition (NER) is an important text analysis task which is not only informative by itself, but is also needed for downstream NLP tasks such as semantic role labeling. In this work, we present an exploration of automatic NER of code-mixed data. We compare our method with existing off-the-shelf NER tools for social media content,and find that our systems outperforms the best baseline by 33.18 {\%} (F1 score).
German and French Neural Supertagging Experiments for LTAG Parsing
Authors: Tatiana Bladier, Andreas van Cranenburgh, Younes Samih, Laura Kallmeyer
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Dependency Parsing, Semantic Parsing, Semantic Role Labeling

We present ongoing work on data-driven parsing of German and French with Lexicalized Tree Adjoining Grammars. We use a supertagging approach combined with deep learning. We show the challenges of extracting LTAG supertags from the French Treebank, introduce the use of left- and right-sister-adjunction, present a neural architecture for the supertagger, and report experiments of n-best supertagging for French and German.
SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags
Authors: Eva Vanmassenhove, Andy Way
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, Named Entity Recognition (NER), NMT, Prepositional Phrase Attachment, Translation, Word Embeddings

In this paper we incorporate semantic supersensetags and syntactic supertag features into EN{--}FR and EN{--}DE factored NMT systems. In experiments on various test sets, we observe that such features (and particularly when combined) help the NMT model training to converge faster and improve the model quality according to the BLEU scores.
Unsupervised Semantic Abstractive Summarization
Authors: Shibhansh Dohare, Vivek Gupta, Harish Karnick
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Abstractive Text Summarization, Sentence Compression

Automatic abstractive summary generation remains a significant open problem for natural language processing. In this work, we develop a novel pipeline for Semantic Abstractive Summarization (SAS). SAS, as introduced by Liu et. al. (2015) first generates an AMR graph of an input story, through which it extracts a summary graph and finally, creates summary sentences from this summary graph. Compared to earlier approaches, we develop a more comprehensive method to generate the story AMR graph using state-of-the-art co-reference resolution and Meta Nodes. Which we then use in a novel unsupervised algorithm based on how humans summarize a piece of text to extract the summary sub-graph. Our algorithm outperforms the state of the art SAS method by 1.7{\%} F1 score in node prediction.
Biomedical Document Retrieval for Clinical Decision Support System
Authors: Jainisha Sankhavara
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Information Retrieval, Learning-To-Rank, Named Entity Recognition (NER), Question Answering, Retrieval, Text Summarization

The availability of huge amount of biomedical literature have opened up new possibilities to apply Information Retrieval and NLP for mining documents from them. In this work, we are focusing on biomedical document retrieval from literature for clinical decision support systems. We compare statistical and NLP based approaches of query reformulation for biomedical document retrieval. Also, we have modeled the biomedical document retrieval as a learning to rank problem. We report initial results for statistical and NLP based query reformulation approaches and learning to rank approach with future direction of research.
A Computational Approach to Feature Extraction for Identification of Suicidal Ideation in Tweets
Authors: Ramit Sawhney, Manch, Prachi a, Raj Singh, Swati Aggarwal
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7

Technological advancements in the World Wide Web and social networks in particular coupled with an increase in social media usage has led to a positive correlation between the exhibition of Suicidal ideation on websites such as Twitter and cases of suicide. This paper proposes a novel supervised approach for detecting suicidal ideation in content on Twitter. A set of features is proposed for training both linear and ensemble classifiers over a dataset of manually annotated tweets. The performance of the proposed methodology is compared against four baselines that utilize varying approaches to validate its utility. The results are finally summarized by reflecting on the effect of the inclusion of the proposed features one by one for suicidal ideation detection.
Exploring Chunk Based Templates for Generating a subset of English Text
Authors: Nikhilesh Bhatnagar, Manish Shrivastava, Radhika Mamidi
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Sentence, Text Generation

Natural Language Generation (NLG) is a research task which addresses the automatic generation of natural language text representative of an input non-linguistic collection of knowledge. In this paper, we address the task of the generation of grammatical sentences in an isolated context given a partial bag-of-words which the generated sentence must contain. We view the task as a search problem (a problem of choice) involving combinations of smaller chunk based templates extracted from a training corpus to construct a complete sentence. To achieve that, we propose a fitness function which we use in conjunction with an evolutionary algorithm as the search procedure to arrive at a potentially grammatical sentence (modeled by the fitness score) which satisfies the input constraints.
Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions
Authors: Eric Wallace, Jordan Boyd-Graber
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Question Answering

Modern question answering systems have been touted as approaching human performance. However, existing question answering datasets are imperfect tests. Questions are written with humans in mind, not computers, and often do not properly expose model limitations. To address this, we develop an adversarial writing setting, where humans interact with trained models and try to break them. This annotation process yields a challenge set, which despite being easy for trivia players to answer, systematically stumps automated question answering systems. Diagnosing model errors on the evaluation data provides actionable insights to explore in developing robust and generalizable question answering systems.
Mixed Feelings: Natural Text Generation with Variable, Coexistent Affective Categories
Authors: Lee Kezar
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Language Modeling, Language Modelling, Sentence, Text Generation

Conversational agents, having the goal of natural language generation, must rely on language models which can integrate emotion into their responses. Recent projects outline models which can produce emotional sentences, but unlike human language, they tend to be restricted to one affective category out of a few. To my knowledge, none allow for the intentional coexistence of multiple emotions on the word or sentence level. Building on prior research which allows for variation in the intensity of a singular emotion, this research proposal outlines an LSTM (Long Short-Term Memory) language model which allows for variation in multiple emotions simultaneously.
Automatic Spelling Correction for Resource-Scarce Languages using Deep Learning
Authors: Pravallika Etoori, Manoj Chinnakotla, Radhika Mamidi
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Deep Learning, Machine Translation, Sentiment Analysis, Spelling Correction, Text Summarization

Spelling correction is a well-known task in Natural Language Processing (NLP). Automatic spelling correction is important for many NLP applications like web search engines, text summarization, sentiment analysis etc. Most approaches use parallel data of noisy and correct word mappings from different sources as training data for automatic spelling correction. Indic languages are resource-scarce and do not have such parallel data due to low volume of queries and non-existence of such prior implementations. In this paper, we show how to build an automatic spelling corrector for resource-scarce languages. We propose a sequence-to-sequence deep learning model which trains end-to-end. We perform experiments on synthetic datasets created for Indic languages, Hindi and Telugu, by incorporating the spelling mistakes committed at character level. A comparative evaluation shows that our model is competitive with the existing spell checking and correction techniques for Indic languages.
Automatic Question Generation using Relative Pronouns and Adverbs
Authors: Payal Khullar, Konigari Rachna, Mukul Hase, Manish Shrivastava
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Descriptive, Dialogue Generation, Information Retrieval, Question Answering, Question Generation, Question-Generation, Reading Comprehension, Sentence

This paper presents a system that automatically generates multiple, natural language questions using relative pronouns and relative adverbs from complex English sentences. Our system is syntax-based, runs on dependency parse information of a single-sentence input, and achieves high accuracy in terms of syntactic correctness, semantic adequacy, fluency and uniqueness. One of the key advantages of our system, in comparison with other rule-based approaches, is that we nearly eliminate the chances of getting a wrong wh-word in the generated question, by fetching the requisite wh-word from the input sentence itself. Depending upon the input, we generate both factoid and descriptive type questions. To the best of our information, the exploitation of wh-pronouns and wh-adverbs to generate questions is novel in the Automatic Question Generation task.
Platforms for Non-speakers Annotating Names in Any Language
Authors: Ying Lin, Cash Costello, Boliang Zhang, Di Lu, Heng Ji, James Mayfield, Paul McNamee
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7

We demonstrate two annotation platforms that allow an English speaker to annotate names for any language without knowing the language. These platforms provided high-quality {'}{`}silver standard{''} annotations for low-resource language name taggers (Zhang et al., 2017) that achieved state-of-the-art performance on two surprise languages (Oromo and Tigrinya) at LoreHLT20171 and ten languages at TAC-KBP EDL2017 (Ji et al., 2017). We discuss strengths and limitations and compare other methods of creating silver- and gold-standard annotations using native speakers. We will make our tools publicly available for research use.
NovelPerspective: Identifying Point of View Characters
Authors: Lyndon White, Roberto Togneri, Wei Liu, Mohammed Bennamoun
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Named Entity Recognition (NER)

We present NovelPerspective: a tool to allow consumers to subset their digital literature, based on point of view (POV) character. Many novels have multiple main characters each with their own storyline running in parallel. A well-known example is George R. R. Martin{'}s novel: {``}A Game of Thrones{''}, and others from that series. Our tool detects the main character that each section is from the POV of, and allows the user to generate a new ebook with only those sections. This gives consumers new options in how they consume their media; allowing them to pursue the storylines sequentially, or skip chapters about characters they find boring. We present two heuristic-based baselines, and two machine learning based methods for the detection of the main character.
Out-of-the-box Universal Romanization Tool uroman
Authors: Ulf Hermjakob, Jonathan May, Kevin Knight
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation

We present uroman, a tool for converting text in myriads of languages and scripts such as Chinese, Arabic and Cyrillic into a common Latin-script representation. The tool relies on Unicode data and other tables, and handles nearly all character sets, including some that are quite obscure such as Tibetan and Tifinagh. uroman converts digital numbers in various scripts to Western Arabic numerals. Romanization enables the application of string-similarity metrics to texts from different scripts without the need and complexity of an intermediate phonetic representation. The tool is freely and publicly available as a Perl script suitable for inclusion in data processing pipelines and as an interactive demo web page.
HarriGT: A Tool for Linking News to Science
Authors: James Ravenscroft, Am Clare, a, Maria Liakata
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Articles, Document Classification, Entity Extraction using GAN, Information Retrieval, Retrieval

Being able to reliably link scientific works to the newspaper articles that discuss them could provide a breakthrough in the way we rationalise and measure the impact of science on our society. Linking these articles is challenging because the language used in the two domains is very different, and the gathering of online resources to align the two is a substantial information retrieval endeavour. We present HarriGT, a semi-automated tool for building corpora of news articles linked to the scientific papers that they discuss. Our aim is to facilitate future development of information-retrieval tools for newspaper/scientific work citation linking. HarriGT retrieves newspaper articles from an archive containing 17 years of UK web content. It also integrates with 3 large external citation networks, leveraging named entity extraction, and document classification to surface relevant examples of scientific literature to the user. We also provide a tuned candidate ranking algorithm to highlight potential links between scientific papers and newspaper articles to the user, in order of likelihood. HarriGT is provided as an open source tool (\url{http://harrigt.xyz}).
Jack the Reader -- A Machine Reading Framework
Authors: Dirk Weissenborn, Pasquale Minervini, Isabelle Augenstein, Johannes Welbl, Tim Rockt{\"a}schel, Matko Bo{\v{s}}njak, Jeff Mitchell, Thomas Demeester, Tim Dettmers, Pontus Stenetorp, Sebastian Riedel
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Articles, Information Retrieval, Link Prediction, Natural Language Inference, Natural Language Understanding, Question Answering, Reading Comprehension

Many Machine Reading and Natural Language Understanding tasks require reading supporting text in order to answer questions. For example, in Question Answering, the supporting text can be newswire or Wikipedia articles; in Natural Language Inference, premises can be seen as the supporting text and hypotheses as questions. Providing a set of useful primitives operating in a single framework of related tasks would allow for expressive modelling, and easier model comparison and replication. To that end, we present Jack the Reader (JACK), a framework for Machine Reading that allows for quick model prototyping by component reuse, evaluation of new models on existing datasets as well as integrating new datasets and applying them on a growing set of implemented baseline models. JACK is currently supporting (but not limited to) three tasks: Question Answering, Natural Language Inference, and Link Prediction. It is developed with the aim of increasing research efficiency and code reuse.
NextGen AML: Distributed Deep Learning based Language Technologies to Augment Anti Money Laundering Investigation
Authors: Jingguang Han, Utsab Barman, Jeremiah Hayes, Jinhua Du, Edward Burgin, Dadong Wan
Date: 2018-07-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Articles, Decision Making, Entity Linking, Relation Extraction, Sentiment Analysis

Most of the current anti money laundering (AML) systems, using handcrafted rules, are heavily reliant on existing structured databases, which are not capable of effectively and efficiently identifying hidden and complex ML activities, especially those with dynamic and time-varying characteristics, resulting in a high percentage of false positives. Therefore, analysts are engaged for further investigation which significantly increases human capital cost and processing time. To alleviate these issues, this paper presents a novel framework for the next generation AML by applying and visualizing deep learning-driven natural language processing (NLP) technologies in a distributed and scalable manner to augment AML monitoring and investigation. The proposed distributed framework performs news and tweet sentiment analysis, entity recognition, relation extraction, entity linking and link analysis on different data sources (e.g. news articles and tweets) to provide additional evidence to human investigators for final decision-making. Each NLP module is evaluated on a task-specific data set, and the overall experiments are performed on synthetic and real-world datasets. Feedback from AML practitioners suggests that our system can reduce approximately 30{\%} time and cost compared to their previous manual approaches of AML investigation.