pwc-archive/papers-with-abstracts huggingface dataset:
Sentiment Analysis for Low Resource Languages: A Study on Informal Indonesian Tweets
Authors: Tuan Anh Le, David Moeljadi, Yasuhide Miura, Tomoko Ohkuma
Date: 2016-12-01 00:00:00
Proceeding: WS 2016 12
Tasks: POS, Sentiment Analysis

This paper describes our attempt to build a sentiment analysis system for Indonesian tweets. With this system, we can study and identify sentiments and opinions in a text or document computationally. We used four thousand manually labeled tweets collected in February and March 2016 to build the model. Because of the variety of content in tweets, we analyze tweets into eight groups in total, including pos(itive), neg(ative), and neu(tral). Finally, we obtained 73.2{\%} accuracy with Long Short Term Memory (LSTM) without normalizer.
A New Approach to Animacy Detection
Authors: Labiba Jahan, Geeticka Chauhan, Mark Finlayson
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Coreference Resolution, Semantic Role Labeling, Word Embeddings, Word Sense Disambiguation

Animacy is a necessary property for a referent to be an agent, and thus animacy detection is useful for a variety of natural language processing tasks, including word sense disambiguation, co-reference resolution, semantic role labeling, and others. Prior work treated animacy as a word-level property, and has developed statistical classifiers to classify words as either animate or inanimate. We discuss why this approach to the problem is ill-posed, and present a new approach based on classifying the animacy of co-reference chains. We show that simple voting approaches to inferring the animacy of a chain from its constituent words perform relatively poorly, and then present a hybrid system merging supervised machine learning (ML) and a small number of hand-built rules to compute the animacy of referring expressions and co-reference chains. This method achieves state of the art performance. The supervised ML component leverages features such as word embeddings over referring expressions, parts of speech, and grammatical and semantic roles. The rules take into consideration parts of speech and the hypernymy structure encoded in WordNet. The system achieves an F1 of 0.88 for classifying the animacy of referring expressions, which is comparable to state of the art results for classifying the animacy of words, and achieves an F1 of 0.75 for classifying the animacy of coreference chains themselves. We release our training and test dataset, which includes 142 texts (all narratives) comprising 156,154 words, 34,698 referring expressions, and 10,941 co-reference chains. We test the method on a subset of the OntoNotes dataset, showing using manual sampling that animacy classification is 90{\%} +/- 2{\%} accurate for coreference chains, and 92{\%} +/- 1{\%} for referring expressions. The data also contains 46 folktales, which present an interesting challenge because they often involve characters who are members of traditionally inanimate classes (e.g., stoves that walk, trees that talk). We show that our system is able to detect the animacy of these unusual referents with an F1 of 0.95.
Zero Pronoun Resolution with Attention-based Neural Network
Authors: Qingyu Yin, Yu Zhang, Wei-Nan Zhang, Ting Liu, William Yang Wang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Chinese Zero Pronoun Resolution

Recent neural network methods for zero pronoun resolution explore multiple models for generating representation vectors for zero pronouns and their candidate antecedents. Typically, contextual information is utilized to encode the zero pronouns since they are simply gaps that contain no actual content. To better utilize contexts of the zero pronouns, we here introduce the self-attention mechanism for encoding zero pronouns. With the help of the multiple hops of attention, our model is able to focus on some informative parts of the associated texts and therefore produces an efficient way of encoding the zero pronouns. In addition, an attention-based recurrent neural network is proposed for encoding candidate antecedents by their contents. Experiment results are encouraging: our proposed attention-based model gains the best performance on the Chinese portion of the OntoNotes corpus, substantially surpasses existing Chinese zero pronoun resolution baseline systems.
They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking
Authors: Ethan Zhou, Jinho D. Choi
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: coreference-resolution, Coreference Resolution, Entity Linking, Machine Translation, Multi-Task Learning, Question Answering

This paper analyzes arguably the most challenging yet under-explored aspect of resolution tasks such as coreference resolution and entity linking, that is the resolution of plural mentions. Unlike singular mentions each of which represents one entity, plural mentions stand for multiple entities. To tackle this aspect, we take the character identification corpus from the SemEval 2018 shared task that consists of entity annotation for singular mentions, and expand it by adding annotation for plural mentions. We then introduce a novel coreference resolution algorithm that selectively creates clusters to handle both singular and plural mentions, and also a deep learning-based entity linking model that jointly handles both types of mentions through multi-task learning. Adjusted evaluation metrics are proposed for these tasks as well to handle the uniqueness of plural mentions. Our experiments show that the new coreference resolution and entity linking models significantly outperform traditional models designed only for singular mentions. To the best of our knowledge, this is the first time that plural mentions are thoroughly analyzed for these two resolution tasks.
Unsupervised Morphology Learning with Statistical Paradigms
Authors: Hongzhi Xu, Mitchell Marcus, Charles Yang, Lyle Ungar
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Information Retrieval, Segmentation, Text Generation

This paper describes an unsupervised model for morphological segmentation that exploits the notion of paradigms, which are sets of morphological categories (e.g., suffixes) that can be applied to a homogeneous set of words (e.g., nouns or verbs). Our algorithm identifies statistically reliable paradigms from the morphological segmentation result of a probabilistic model, and chooses reliable suffixes from them. The new suffixes can be fed back iteratively to improve the accuracy of the probabilistic model. Finally, the unreliable paradigms are subjected to pruning to eliminate unreliable morphological relations between words. The paradigm-based algorithm significantly improves segmentation accuracy. Our method achieves start-of-the-art results on experiments using the Morpho-Challenge data, including English, Turkish, and Finnish.
Neural Transition-based String Transduction for Limited-Resource Setting in Morphology
Authors: Peter Makarov, Simon Clematide
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Lemmatization, Machine Translation, Morphological Inflection

We present a neural transition-based model that uses a simple set of edit actions (copy, delete, insert) for morphological transduction tasks such as inflection generation, lemmatization, and reinflection. In a large-scale evaluation on four datasets and dozens of languages, our approach consistently outperforms state-of-the-art systems on low and medium training-set sizes and is competitive in the high-resource setting. Learning to apply a generic copy action enables our approach to generalize quickly from a few data points. We successfully leverage minimum risk training to compensate for the weaknesses of MLE parameter learning and neutralize the negative effects of training a pipeline with a separate character aligner.
Sprucing up the trees -- Error detection in treebanks
Authors: Ines Rehbein, Josef Ruppenhofer
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Active Learning, Bayesian Inference, Domain Adaptation, Named Entity Recognition (NER)

We present a method for detecting annotation errors in manually and automatically annotated dependency parse trees, based on ensemble parsing in combination with Bayesian inference, guided by active learning. We evaluate our method in different scenarios: (i) for error detection in dependency treebanks and (ii) for improving parsing accuracy on in- and out-of-domain data.
RNN Simulations of Grammaticality Judgments on Long-distance Dependencies
Authors: Shammur Absar Chowdhury, Roberto Zamparelli
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Language Modeling, Language Modelling

The paper explores the ability of LSTM networks trained on a language modeling task to detect linguistic structures which are ungrammatical due to extraction violations (extra arguments and subject-relative clause island violations), and considers its implications for the debate on language innatism. The results show that the current RNN model can correctly classify (un)grammatical sentences, in certain conditions, but it is sensitive to linguistic processing factors and probably ultimately unable to induce a more abstract notion of grammaticality, at least in the domain we tested.
Incorporating Image Matching Into Knowledge Acquisition for Event-Oriented Relation Recognition
Authors: Yu Hong, Yang Xu, Huibin Ruan, Bowei Zou, Jianmin Yao, Guodong Zhou
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Relation

Event relation recognition is a challenging language processing task. It is required to determine the relation class of a pair of query events, such as causality, under the condition that there isn{'}t any reliable clue for use. We follow the traditional statistical approach in this paper, speculating the relation class of the target events based on the relation-class distributions on the similar events. There is minimal supervision used during the speculation process. In particular, we incorporate image processing into the acquisition of similar event instances, including the utilization of images for visually representing event scenes, and the use of the neural network based image matching for approximate calculation between events. We test our method on the ACE-R2 corpus and compared our model with the fully-supervised neural network models. Experimental results show that we achieve a comparable performance to CNN while slightly better than LSTM.
Simple Neologism Based Domain Independent Models to Predict Year of Authorship
Authors: Vivek Kulkarni, Yingtao Tian, D, Parth iwala, Steve Skiena
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Domain Adaptation, Information Retrieval

We present domain independent models to date documents based only on neologism usage patterns. Our models capture patterns of neologism usage over time to date texts, provide insights into temporal locality of word usage over a span of 150 years, and generalize to various domains like News, Fiction, and Non-Fiction with competitive performance. Quite intriguingly, we show that by modeling only the distribution of usage counts over neologisms (the model being agnostic of the particular words themselves), we achieve competitive performance using several orders of magnitude fewer features (only 200 input features) compared to state of the art models some of which use 200K features.
Neural Math Word Problem Solver with Reinforcement Learning
Authors: Danqing Huang, Jing Liu, Chin-Yew Lin, Jian Yin
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Feature Engineering, Math, Math Word Problem Solving, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Sequence-to-sequence model has been applied to solve math word problems. The model takes math problem descriptions as input and generates equations as output. The advantage of sequence-to-sequence model requires no feature engineering and can generate equations that do not exist in training data. However, our experimental analysis reveals that this model suffers from two shortcomings: (1) generate spurious numbers; (2) generate numbers at wrong positions. In this paper, we propose incorporating copy and alignment mechanism to the sequence-to-sequence model (namely CASS) to address these shortcomings. To train our model, we apply reinforcement learning to directly optimize the solution accuracy. It overcomes the {``}train-test discrepancy{''} issue of maximum likelihood estimation, which uses the surrogate objective of maximizing equation likelihood during training while the evaluation metric is solution accuracy (non-differentiable) at test time. Furthermore, to explore the effectiveness of our neural model, we use our model output as a feature and incorporate it into the feature-based model. Experimental results show that (1) The copy and alignment mechanism is effective to address the two issues; (2) Reinforcement learning leads to better performance than maximum likelihood on this task; (3) Our neural model is complementary to the feature-based model and their combination significantly outperforms the state-of-the-art results.
Personalizing Lexical Simplification
Authors: John Lee, Chak Yan Yeung
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Complex Word Identification, Lexical Simplification

A lexical simplification (LS) system aims to substitute complex words with simple words in a text, while preserving its meaning and grammaticality. Despite individual users{'} differences in vocabulary knowledge, current systems do not consider these variations; rather, they are trained to find one optimal substitution or ranked list of substitutions for all users. We evaluate the performance of a state-of-the-art LS system on individual learners of English at different proficiency levels, and measure the benefits of using complex word identification (CWI) models to personalize the system. Experimental results show that even a simple personalized CWI model, based on graded vocabulary lists, can help the system avoid some unnecessary simplifications and produce more readable output.
From Text to Lexicon: Bridging the Gap between Word Embeddings and Lexical Resources
Authors: Ilia Kuznetsov, Iryna Gurevych
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Coreference Resolution, Lemmatization, POS, Word Embeddings

Distributional word representations (often referred to as word embeddings) are omnipresent in modern NLP. Early work has focused on building representations for word types, and recent studies show that lemmatization and part of speech (POS) disambiguation of targets in isolation improve the performance of word embeddings on a range of downstream tasks. However, the reasons behind these improvements, the qualitative effects of these operations and the combined performance of lemmatized and POS disambiguated targets are less studied. This work aims to close this gap and puts previous findings into a general perspective. We examine the effect of lemmatization and POS typing on word embedding performance in a novel resource-based evaluation scenario, as well as on standard similarity benchmarks. We show that these two operations have complimentary qualitative and vocabulary-level effects and are best used in combination. We find that the improvement is more pronounced for verbs and show how lemmatization and POS typing implicitly target some of the verb-specific issues. We claim that the observed improvement is a result of better conceptual alignment between word embeddings and lexical resources, stressing the need for conceptually plausible modeling of word embedding targets.
Lexi: A tool for adaptive, personalized text simplification
Authors: Joachim Bingel, Gustavo Paetzold, Anders S{\o}gaard
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Lexical Simplification, Text Simplification

Most previous research in text simplification has aimed to develop generic solutions, assuming very homogeneous target audiences with consistent intra-group simplification needs. We argue that this assumption does not hold, and that instead we need to develop simplification systems that adapt to the individual needs of specific users. As a first step towards personalized simplification, we propose a framework for adaptive lexical simplification and introduce Lexi, a free open-source and easily extensible tool for adaptive, personalized text simplification. Lexi is easily installed as a browser extension, enabling easy access to the service for its users.
Identifying Emergent Research Trends by Key Authors and Phrases
Authors: Shenhao Jiang, Animesh Prasad, Min-Yen Kan, Kazunari Sugiyama
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Articles, Information Retrieval, Topic Models

Identifying emergent research trends is a key issue for both primary researchers as well as secondary research managers. Such processes can uncover the historical development of an area, and yield insight on developing topics. We propose an embedded trend detection framework for this task which incorporates our bijunctive hypothesis that important phrases are written by important authors within a field and vice versa. By ranking both author and phrase information in a multigraph, our method jointly determines key phrases and authoritative authors. We represent this intermediate output as phrasal embeddings, and feed this to a recurrent neural network (RNN) to compute trend scores that identify research trends. Over two large datasets of scientific articles, we demonstrate that our approach successfully detects past trends from the field, outperforming baselines based solely on text centrality or citation.
Embedding WordNet Knowledge for Textual Entailment
Authors: Yunshi Lan, Jing Jiang
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Feature Engineering, Lexical Entailment, Natural Language Inference

In this paper, we study how we can improve a deep learning approach to textual entailment by incorporating lexical entailment relations from WordNet. Our idea is to embed the lexical entailment knowledge contained in WordNet in specially-learned word vectors, which we call {``}entailment vectors.{''} We present a standard neural network model and a novel set-theoretic model to learn these entailment vectors from word pairs with known lexical entailment relations derived from WordNet. We further incorporate these entailment vectors into a decomposable attention model for textual entailment and evaluate the model on the SICK and the SNLI dataset. We find that using these special entailment word vectors, we can significantly improve the performance of textual entailment compared with a baseline that uses only standard word2vec vectors. The final performance of our model is close to or above the state of the art, but our method does not rely on any manually-crafted rules or extensive syntactic features.
Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases
Authors: Hailong Jin, Lei Hou, Juanzi Li, Tiansi Dong
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Entity Linking, Entity Typing, Knowledge Base Completion, Question Answering, Relation Extraction, Vocal Bursts Type Prediction

Fine-grained entity typing aims at identifying the semantic type of an entity in KB. Type information is very important in knowledge bases, but are unfortunately incomplete even in some large knowledge bases. Limitations of existing methods are either ignoring the structure and type information in KB or requiring large scale annotated corpus. To address these issues, we propose an attributed and predictive entity embedding method, which can fully utilize various kinds of information comprehensively. Extensive experiments on two real DBpedia datasets show that our proposed method significantly outperforms 8 state-of-the-art methods, with 4.0{\%} and 5.2{\%} improvement in Mi-F1 and Ma-F1, respectively.
Joint Learning from Labeled and Unlabeled Data for Information Retrieval
Authors: Bo Li, Ping Cheng, Le Jia
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Information Retrieval, Representation Learning, Retrieval, Speech Recognition, Word Embeddings

Recently, a significant number of studies have focused on neural information retrieval (IR) models. One category of works use unlabeled data to train general word embeddings based on term proximity, which can be integrated into traditional IR models. The other category employs labeled data (e.g. click-through data) to train end-to-end neural IR models consisting of layers for target-specific representation learning. The latter idea accounts better for the IR task and is favored by recent research works, which is the one we will follow in this paper. We hypothesize that general semantics learned from unlabeled data can complement task-specific representation learned from labeled data of limited quality, and that a combination of the two is favorable. To this end, we propose a learning framework which can benefit from both labeled and more abundant unlabeled data for representation learning in the context of IR. Through a joint learning fashion in a single neural framework, the learned representation is optimized to minimize both the supervised loss on query-document matching and the unsupervised loss on text reconstruction. Standard retrieval experiments on TREC collections indicate that the joint learning methodology leads to significant better performance of retrieval over several strong baselines for IR.
Modeling the Readability of German Targeting Adults and Children: An empirically broad analysis and its cross-corpus validation
Authors: Zarah Wei{\ss}, Detmar Meurers
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Binary Classification, Cross-corpus, Information Retrieval, Text Simplification

We analyze two novel data sets of German educational media texts targeting adults and children. The analysis is based on 400 automatically extracted measures of linguistic complexity from a wide range of linguistic domains. We show that both data sets exhibit broad linguistic adaptation to the target audience, which generalizes across both data sets. Our most successful binary classification model for German readability robustly shows high accuracy between 89.4{\%}{--}98.9{\%} for both data sets. To our knowledge, this comprehensive German readability model is the first for which robust cross-corpus performance has been shown. The research also contributes resources for German readability assessment that are externally validated as successful for different target audiences: we compiled a new corpus of German news broadcast subtitles, the Tagesschau/Logo corpus, and crawled a GEO/GEOlino corpus substantially enlarging the data compiled by Hancke et al. 2012.
Automatic Assessment of Conceptual Text Complexity Using Knowledge Graphs
Authors: Sanja {\v{S}}tajner, Ioana Hulpu{\c{s}}
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Binary Classification, Knowledge Graphs, Text Simplification

Complexity of texts is usually assessed only at the lexical and syntactic levels. Although it is known that conceptual complexity plays a significant role in text understanding, no attempts have been made at assessing it automatically. We propose to automatically estimate the conceptual complexity of texts by exploiting a number of graph-based measures on a large knowledge base. By using a high-quality language learners corpus for English, we show that graph-based measures of individual text concepts, as well as the way they relate to each other in the knowledge graph, have a high discriminative power when distinguishing between two versions of the same text. Furthermore, when used as features in a binary classification task aiming to choose the simpler of two versions of the same text, our measures achieve high performance even in a default setup.