pwc-archive/papers-with-abstracts huggingface dataset:
Using Word Embeddings for Unsupervised Acronym Disambiguation
Authors: Jean Charbonnier, Christian Wartena
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Image Captioning, Word Embeddings, Word Sense Disambiguation

Scientific papers from all disciplines contain many abbreviations and acronyms. In many cases these acronyms are ambiguous. We present a method to choose the contextual correct definition of an acronym that does not require training for each acronym and thus can be applied to a large number of different acronyms with only few instances. We constructed a set of 19,954 examples of 4,365 ambiguous acronyms from image captions in scientific papers along with their contextually correct definition from different domains. We learn word embeddings for all words in the corpus and compare the averaged context vector of the words in the expansion of an acronym with the weighted average vector of the words in the context of the acronym. We show that this method clearly outperforms (classical) cosine similarity. Furthermore, we show that word embeddings learned from a 1 billion word corpus of scientific texts outperform word embeddings learned on much large general corpora.
Indigenous language technologies in Canada: Assessment, challenges, and successes
Authors: Patrick Littell, Anna Kazantseva, Rol Kuhn, Aidan Pine, Antti Arppe, Christopher Cox, Marie-Odile Junker
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Optical Character Recognition, Optical Character Recognition (OCR), speaker-diarization, Speaker Diarization, speech-recognition, Speech Recognition, Speech Synthesis, Translation, Transliteration

In this article, we discuss which text, speech, and image technologies have been developed, and would be feasible to develop, for the approximately 60 Indigenous languages spoken in Canada. In particular, we concentrate on technologies that may be feasible to develop for most or all of these languages, not just those that may be feasible for the few most-resourced of these. We assess past achievements and consider future horizons for Indigenous language transliteration, text prediction, spell-checking, approximate search, machine translation, speech recognition, speaker diarization, speech synthesis, optical character recognition, and computer-aided language learning.
Pluralizing Nouns across Agglutinating Bantu Languages
Authors: Joan Byamugisha, C. Maria Keet, Brian DeRenzi
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Text Generation

Text generation may require the pluralization of nouns, such as in context-sensitive user interfaces and in natural language generation more broadly. While this has been solved for the widely-used languages, this is still a challenge for the languages in the Bantu language family. Pluralization results obtained for isiZulu and Runyankore showed there were similarities in approach, including the need to combine syntax with semantics, despite belonging to different language zones. This suggests that bootstrapping and generalizability might be feasible. We investigated this systematically for seven languages across three different Guthrie language zones. The first outcome is that Meinhof{'}s 1948 specification of the noun classes are indeed inadequate for computational purposes for all examined languages, due to non-determinism in prefixes, and we thus redefined the characteristic noun class tables of 29 noun classes into 53. The second main result is that the generic pluralizer achieved over 93{\%} accuracy in coverage testing and over 94{\%} on a random sample. This is comparable to the language-specific isiZulu and Runyankore pluralizers.
Automatically Extracting Qualia Relations for the Rich Event Ontology
Authors: Ghazaleh Kazeminejad, Claire Bonial, Susan Windisch Brown, Martha Palmer
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Semantic Role Labeling, World Knowledge

Commonsense, real-world knowledge about the events that entities or {``}things in the world{''} are typically involved in, as well as part-whole relationships, is valuable for allowing computational systems to draw everyday inferences about the world. Here, we focus on automatically extracting information about (1) the events that typically bring about certain entities (origins), (2) the events that are the typical functions of entities, and (3) part-whole relationships in entities. These correspond to the agentive, telic and constitutive qualia central to the Generative Lexicon. We describe our motivations and methods for extracting these qualia relations from the Suggested Upper Merged Ontology (SUMO) and show that human annotators overwhelmingly find the information extracted to be reasonable. Because ontologies provide a way of structuring this information and making it accessible to agents and computational systems generally, efforts are underway to incorporate the extracted information to an ontology hub of Natural Language Processing semantic role labeling resources, the Rich Event Ontology.
Using Formulaic Expressions in Writing Assistance Systems
Authors: Kenichi Iwatsuki, Akiko Aizawa
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Sentence

Formulaic expressions (FEs) used in scholarly papers, such as {`}there has been little discussion about{'}, are helpful for non-native English speakers. However, it is time-consuming for users to manually search for an appropriate expression every time they want to consult FE dictionaries. For this reason, we tackle the task of semantic searches of FE dictionaries. At the start of our research, we identified two salient difficulties in this task. First, the paucity of example sentences in existing FE dictionaries results in a shortage of context information, which is necessary for acquiring semantic representation of FEs. Second, while a semantic category label is assigned to each FE in many FE dictionaries, it is difficult to predict the labels from user input, forcing users to manually designate the semantic category when searching. To address these difficulties, we propose a new framework for semantic searches of FEs and propose a new method to leverage both existing dictionaries and domain sentence corpora. Further, we expand an existing FE dictionary to consider building a more comprehensive and domain-specific FE dictionary and to verify the effectiveness of our method.
What's in Your Embedding, And How It Predicts Task Performance
Authors: Anna Rogers, Shashwath Hosur Ananthakrishna, Anna Rumshisky
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Named Entity Recognition (NER), Semantic Role Labeling, Word Embeddings

Attempts to find a single technique for general-purpose intrinsic evaluation of word embeddings have so far not been successful. We present a new approach based on scaled-up qualitative analysis of word vector neighborhoods that quantifies interpretable characteristics of a given model (e.g. its preference for synonyms or shared morphological forms as nearest neighbors). We analyze 21 such factors and show how they correlate with performance on 14 extrinsic and intrinsic task datasets (and also explain the lack of correlation between some of them). Our approach enables multi-faceted evaluation, parameter search, and generally {--} a more principled, hypothesis-driven approach to development of distributional semantic representations.
Word Sense Disambiguation Based on Word Similarity Calculation Using Word Vector Representation from a Knowledge-based Graph
Authors: Dongsuk O, Sunjae Kwon, Kyungsun Kim, Youngjoong Ko
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Information Retrieval, Machine Translation, Word Sense Disambiguation, Word Similarity

Word sense disambiguation (WSD) is the task to determine the word sense according to its context. Many existing WSD studies have been using an external knowledge-based unsupervised approach because it has fewer word set constraints than supervised approaches requiring training data. In this paper, we propose a new WSD method to generate the context of an ambiguous word by using similarities between an ambiguous word and words in the input document. In addition, to leverage our WSD method, we further propose a new word similarity calculation method based on the semantic network structure of BabelNet. We evaluate the proposed methods on the SemEval-13 and SemEval-15 for English WSD dataset. Experimental results demonstrate that the proposed WSD method significantly improves the baseline WSD method. Furthermore, our WSD system outperforms the state-of-the-art WSD systems in the Semeval-13 dataset. Finally, it has higher performance than the state-of-the-art unsupervised knowledge-based WSD system in the average performance of both datasets.
Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator
Authors: Badri Narayana Patro, Vinod Kumar Kurmi, S. Kumar, eep, Vinay Namboodiri
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Machine Reading Comprehension, Machine Translation, Paraphrase Generation, Reading Comprehension, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings, Sentiment Analysis

In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple method in the context of solving the paraphrase generation task. If we use a sequential encoder-decoder model for generating paraphrase, we would like the generated paraphrase to be semantically close to the original sentence. One way to ensure this is by adding constraints for true paraphrase embeddings to be close and unrelated paraphrase candidate sentence embeddings to be far. This is ensured by using a sequential pair-wise discriminator that shares weights with the encoder that is trained with a suitable loss function. Our loss function penalizes paraphrase sentence embedding distances from being too large. This loss is used in combination with a sequential encoder-decoder network. We also validated our method by evaluating the obtained embeddings for a sentiment analysis task. The proposed method results in semantic embeddings and outperforms the state-of-the-art on the paraphrase generation and sentiment analysis task on standard datasets. These results are also shown to be statistically significant.
A Reassessment of Reference-Based Grammatical Error Correction Metrics
Authors: Shamil Chollampatt, Hwee Tou Ng
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Grammatical Error Correction, Machine Translation, Sentence

Several metrics have been proposed for evaluating grammatical error correction (GEC) systems based on grammaticality, fluency, and adequacy of the output sentences. Previous studies of the correlation of these metrics with human quality judgments were inconclusive, due to the lack of appropriate significance tests, discrepancies in the methods, and choice of datasets used. In this paper, we re-evaluate reference-based GEC metrics by measuring the system-level correlations with humans on a large dataset of human judgments of GEC outputs, and by properly conducting statistical significance tests. Our results show no significant advantage of GLEU over MaxMatch (M2), contradicting previous studies that claim GLEU to be superior. For a finer-grained analysis, we additionally evaluate these metrics for their agreement with human judgments at the sentence level. Our sentence-level analysis indicates that comparing GLEU and M2, one metric may be more useful than the other depending on the scenario. We further qualitatively analyze these metrics and our findings show that apart from being less interpretable and non-deterministic, GLEU also produces counter-intuitive scores in commonly occurring test examples.
A Full End-to-End Semantic Role Labeler, Syntactic-agnostic Over Syntactic-aware?
Authors: Jiaxun Cai, Shexia He, Zuchao Li, Hai Zhao
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Machine Translation, Question Answering, Semantic Parsing, Semantic Role Labeling, Sentence

Semantic role labeling (SRL) is to recognize the predicate-argument structure of a sentence, including subtasks of predicate disambiguation and argument labeling. Previous studies usually formulate the entire SRL problem into two or more subtasks. For the first time, this paper introduces an end-to-end neural model which unifiedly tackles the predicate disambiguation and the argument labeling in one shot. Using a biaffine scorer, our model directly predicts all semantic role labels for all given word pairs in the sentence without relying on any syntactic parse information. Specifically, we augment the BiLSTM encoder with a non-linear transformation to further distinguish the predicate and the argument in a given sentence, and model the semantic role labeling process as a word pair classification task by employing the biaffine attentional mechanism. Though the proposed model is syntax-agnostic with local decoder, it outperforms the state-of-the-art syntax-aware SRL systems on the CoNLL-2008, 2009 benchmarks for both English and Chinese. To our best knowledge, we report the first syntax-agnostic SRL model that surpasses all known syntax-aware models.
Authorship Attribution By Consensus Among Multiple Features
Authors: Jagadeesh Patchala, Raj Bhatnagar
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Author Attribution, Authorship Attribution

Most existing research on authorship attribution uses various lexical, syntactic and semantic features. In this paper we demonstrate an effective template-based approach for combining various syntactic features of a document for authorship analysis. The parse-tree based features that we propose are independent of the topic of a document and reflect the innate writing styles of authors. We show that the use of templates including sub-trees of parse trees in conjunction with other syntactic features result in improved author attribution rates. Another contribution is the demonstration that Dempster{'}s rule based combination of evidence from syntactic features performs better than other evidence-combination methods. We also demonstrate that our methodology works well for the case where actual author is not included in the candidate author set.
Modeling with Recurrent Neural Networks for Open Vocabulary Slots
Authors: Jun-Seong Kim, Junghoe Kim, SeungUn Park, Kwangyong Lee, Yoonju Lee
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Goal-Oriented Dialogue Systems, Intent Detection, Language Modeling, Language Modelling, Multi-Task Learning, Sentence, Slot Filling, Spoken Dialogue Systems, Spoken Language Understanding, Word Embeddings

Dealing with {`}open-vocabulary{'} slots has been among the challenges in the natural language area. While recent studies on attention-based recurrent neural network (RNN) models have performed well in completing several language related tasks such as spoken language understanding and dialogue systems, there has been a lack of attempts to address filling slots that take on values from a virtually unlimited set. In this paper, we propose a new RNN model that can capture the vital concept: Understanding the role of a word may vary according to how long a reader focuses on a particular part of a sentence. The proposed model utilizes a long-term aware attention structure, positional encoding primarily considering the relative distance between words, and multi-task learning of a character-based language model and an intent detection model. We show that the model outperforms the existing RNN models with respect to discovering {`}open-vocabulary{'} slots without any external information, such as a named entity database or knowledge base. In particular, we confirm that it performs better with a greater number of slots in a dataset, including unknown words, by evaluating the models on a dataset of several domains. In addition, the proposed model also demonstrates superior performance with regard to intent detection.
Challenges and Opportunities of Applying Natural Language Processing in Business Process Management
Authors: Han van der Aa, Josep Carmona, Henrik Leopold, Jan Mendling, Llu{\'\i}s Padr{\'o}
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Management, Position

The Business Process Management (BPM) field focuses in the coordination of labor so that organizational processes are smoothly executed in a way that products and services are properly delivered. At the same time, NLP has reached a maturity level that enables its widespread application in many contexts, thanks to publicly available frameworks. In this position paper, we show how NLP has potential in raising the benefits of BPM practices at different levels. Instead of being exhaustive, we show selected key challenges were a successful application of NLP techniques would facilitate the automation of particular tasks that nowadays require a significant effort to accomplish. Finally, we report on applications that consider both the process perspective and its enhancement through NLP.
Novelty Goes Deep. A Deep Neural Solution To Document Level Novelty Detection
Authors: Tirthankar Ghosal, Vignesh Edithal, Asif Ekbal, Pushpak Bhattacharyya, George Tsatsaronis, Srinivasa Satya Sameer Kumar Chivukula
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Document Summarization, Feature Engineering, Information Retrieval, Novelty Detection

The rapid growth of documents across the web has necessitated finding means of discarding redundant documents and retaining novel ones. Capturing redundancy is challenging as it may involve investigating at a deep semantic level. Techniques for detecting such semantic redundancy at the document level are scarce. In this work we propose a deep Convolutional Neural Networks (CNN) based model to classify a document as novel or redundant with respect to a set of relevant documents already seen by the system. The system is simple and do not require any manual feature engineering. Our novel scheme encodes relevant and relative information from both source and target texts to generate an intermediate representation which we coin as the Relative Document Vector (RDV). The proposed method outperforms the existing state-of-the-art on a document-level novelty detection dataset by a margin of âˆ¼5{\%} in terms of accuracy. We further demonstrate the effectiveness of our approach on a standard paraphrase detection dataset where paraphrased passages closely resemble to semantically redundant documents.
What represents ``style'' in authorship attribution?
Authors: Kalaivani Sundararajan, Damon Woodard
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Authorship Attribution, Language Modeling, Language Modelling, Sentence

Authorship attribution typically uses all information representing both content and style whereas attribution based only on stylistic aspects may be robust in cross-domain settings. This paper analyzes different linguistic aspects that may help represent style. Specifically, we study the role of syntax and lexical words (nouns, verbs, adjectives and adverbs) in representing style. We use a purely syntactic language model to study the significance of sentence structures in both single-domain and cross-domain attribution, i.e. cross-topic and cross-genre attribution. We show that syntax may be helpful for cross-genre attribution while cross-topic attribution and single-domain may benefit from additional lexical information. Further, pure syntactic models may not be effective by themselves and need to be used in combination with other robust models. To study the role of word choice, we perform attribution by masking all words or specific topic words corresponding to nouns, verbs, adjectives and adverbs. Using a single-domain dataset, IMDB1M reviews, we demonstrate the heavy influence of common nouns and proper nouns in attribution, thereby highlighting topic interference. Using cross-domain Guardian10 dataset, we show that some common nouns, verbs, adjectives and adverbs may help with stylometric attribution as demonstrated by masking topic words corresponding to these parts-of-speech. As expected, it was observed that proper nouns are heavily influenced by content and cross-domain attribution will benefit from completely masking them.
Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction
Authors: Junwen Duan, Yue Zhang, Xiao Ding, Ching-Yun Chang, Ting Liu
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Information Retrieval, Sentence, Stock Market Prediction

Texts from the Internet serve as important data sources for financial market modeling. Early statistical approaches rely on manually defined features to capture lexical, sentiment and event information, which suffers from feature sparsity. Recent work has considered learning dense representations for news titles and abstracts. Compared to news titles, full documents can contain more potentially helpful information, but also noise compared to events and sentences, which has been less investigated in previous work. To fill this gap, we propose a novel target-specific abstract-guided news document representation model. The model uses a target-sensitive representation of the news abstract to weigh sentences in the news content, so as to select and combine the most informative sentences for market modeling. Results show that document representations can give better performance for estimating cumulative abnormal returns of companies when compared to titles and abstracts. Our model is especially effective when it used to combine information from multiple document sources compared to the sentence-level baselines.
Model-Free Context-Aware Word Composition
Authors: Bo An, Xianpei Han, Le Sun
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dimensionality Reduction, Learning Word Embeddings, Machine Translation, model, Representation Learning, Sentence, Sentiment Analysis, Word Embeddings, Word Sense Disambiguation

Word composition is a promising technique for representation learning of large linguistic units (e.g., phrases, sentences and documents). However, most of the current composition models do not take the ambiguity of words and the context outside of a linguistic unit into consideration for learning representations, and consequently suffer from the inaccurate representation of semantics. To address this issue, we propose a model-free context-aware word composition model, which employs the latent semantic information as global context for learning representations. The proposed model attempts to resolve the word sense disambiguation and word composition in a unified framework. Extensive evaluation shows consistent improvements over various strong word representation/composition models at different granularities (including word, phrase and sentence), demonstrating the effectiveness of our proposed method.
Towards a unified framework for bilingual terminology extraction of single-word and multi-word terms
Authors: Jingshu Liu, Emmanuel Morin, Pe{\~n}a Saldarriaga
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Word Embeddings

Extracting a bilingual terminology for multi-word terms from comparable corpora has not been widely researched. In this work we propose a unified framework for aligning bilingual terms independently of the term lengths. We also introduce some enhancements to the context-based and the neural network based approaches. Our experiments show the effectiveness of our enhancements of previous works and the system can be adapted in specialized domains.