pwc-archive/papers-with-abstracts huggingface dataset:
A Hybrid Approach to Automatic Corpus Generation for Chinese Spelling Check
Authors: Dingmin Wang, Yan Song, Jing Li, Jialong Han, Haisong Zhang
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Language Modeling, Language Modelling, Optical Character Recognition (OCR), Part-Of-Speech Tagging

Chinese spelling check (CSC) is a challenging yet meaningful task, which not only serves as a preprocessing in many natural language processing(NLP) applications, but also facilitates reading and understanding of running texts in peoples{'} daily lives. However, to utilize data-driven approaches for CSC, there is one major limitation that annotated corpora are not enough in applying algorithms and building models. In this paper, we propose a novel approach of constructing CSC corpus with automatically generated spelling errors, which are either visually or phonologically resembled characters, corresponding to the OCR- and ASR-based methods, respectively. Upon the constructed corpus, different models are trained and evaluated for CSC with respect to three standard test sets. Experimental results demonstrate the effectiveness of the corpus, therefore confirm the validity of our approach.
Neural Quality Estimation of Grammatical Error Correction
Authors: Shamil Chollampatt, Hwee Tou Ng
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Grammatical Error Correction, Machine Translation, Re-Ranking

Grammatical error correction (GEC) systems deployed in language learning environments are expected to accurately correct errors in learners{'} writing. However, in practice, they often produce spurious corrections and fail to correct many errors, thereby misleading learners. This necessitates the estimation of the quality of output sentences produced by GEC systems so that instructors can selectively intervene and re-correct the sentences which are poorly corrected by the system and ensure that learners get accurate feedback. We propose the first neural approach to automatic quality estimation of GEC output sentences that does not employ any hand-crafted features. Our system is trained in a supervised manner on learner sentences and corresponding GEC system outputs with quality score labels computed using human-annotated references. Our neural quality estimation models for GEC show significant improvements over a strong feature-based baseline. We also show that a state-of-the-art GEC system can be improved when quality scores are used as features for re-ranking the N-best candidates.
Transferring from Formal Newswire Domain with Hypernet for Twitter POS Tagging
Authors: Tao Gui, Qi Zhang, Jingjing Gong, Minlong Peng, Di Liang, Keyu Ding, Xuanjing Huang
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Domain Adaptation, Multi-Task Learning, Part-Of-Speech Tagging, POS, POS Tagging, Stock Prediction

Part-of-Speech (POS) tagging for Twitter has received considerable attention in recent years. Because most POS tagging methods are based on supervised models, they usually require a large amount of labeled data for training. However, the existing labeled datasets for Twitter are much smaller than those for newswire text. Hence, to help POS tagging for Twitter, most domain adaptation methods try to leverage newswire datasets by learning the shared features between the two domains. However, from a linguistic perspective, Twitter users not only tend to mimic the formal expressions of traditional media, like news, but they also appear to be developing linguistically informal styles. Therefore, POS tagging for the formal Twitter context can be learned together with the newswire dataset, while POS tagging for the informal Twitter context should be learned separately. To achieve this task, in this work, we propose a hypernetwork-based method to generate different parameters to separately model contexts with different expression styles. Experimental results on three different datasets show that our approach achieves better performance than state-of-the-art methods in most cases.
A Challenge Set and Methods for Noun-Verb Ambiguity
Authors: Ali Elkahky, Kellie Webster, Daniel Andor, Emily Pitler
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis, Translation, Word Embeddings

English part-of-speech taggers regularly make egregious errors related to noun-verb ambiguity, despite having achieved 97{\%}+ accuracy on the WSJ Penn Treebank since 2002. These mistakes have been difficult to quantify and make taggers less useful to downstream tasks such as translation and text-to-speech synthesis. This paper creates a new dataset of over 30,000 naturally-occurring non-trivial examples of noun-verb ambiguity. Taggers within 1{\%} of each other when measured on the WSJ have accuracies ranging from 57{\%} to 75{\%} accuracy on this challenge set. Enhancing the strongest existing tagger with contextual word embeddings and targeted training data improves its accuracy to 89{\%}, a 14{\%} absolute (52{\%} relative) improvement. Downstream, using just this enhanced tagger yields a 28{\%} reduction in error over the prior best learned model for homograph disambiguation for textto-speech synthesis.
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection
Authors: Devamanyu Hazarika, Soujanya Poria, Rada Mihalcea, Erik Cambria, Roger Zimmermann
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Emotion Recognition, Emotion Recognition in Conversation, General Classification, Multimodal Emotion Recognition, regression

Emotion recognition in conversations is crucial for building empathetic machines. Present works in this domain do not explicitly consider the inter-personal influences that thrive in the emotional dynamics of dialogues. To this end, we propose Interactive COnversational memory Network (ICON), a multimodal emotion detection framework that extracts multimodal features from conversational videos and hierarchically models the self- and inter-speaker emotional influences into global memories. Such memories generate contextual summaries which aid in predicting the emotional orientation of utterance-videos. Our model outperforms state-of-the-art networks on multiple classification and regression tasks in two benchmark datasets.
Grounding Semantic Roles in Images
Authors: Carina Silberer, Manfred Pinkal
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Image Captioning, Question Answering, Semantic Role Labeling

We address the task of visual semantic role labeling (vSRL), the identification of the participants of a situation or event in a visual scene, and their labeling with their semantic relations to the event or situation. We render candidate participants as image regions of objects, and train a model which learns to ground roles in the regions which depict the corresponding participant. Experimental results demonstrate that we can train a vSRL model without reliance on prohibitive image-based role annotations, by utilizing noisy data which we extract automatically from image captions using a linguistic SRL system. Furthermore, our model induces frame{---}semantic visual representations, and their comparison to previous work on supervised visual verb sense disambiguation yields overall better results.
Commonsense Justification for Action Explanation
Authors: Shaohua Yang, Qiaozi Gao, Sari Sadiya, Joyce Chai
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decision Making

To enable collaboration and communication between humans and agents, this paper investigates learning to acquire commonsense evidence for action justification. In particular, we have developed an approach based on the generative Conditional Variational Autoencoder(CVAE) that models object relations/attributes of the world as latent variables and jointly learns a performer that predicts actions and an explainer that gathers commonsense evidence to justify the action. Our empirical results have shown that, compared to a typical attention-based model, CVAE achieves significantly higher performance in both action prediction and justification. A human subject study further shows that the commonsense evidence gathered by CVAE can be communicated to humans to achieve a significantly higher common ground between humans and agents.
Grounding language acquisition by training semantic parsers using captioned videos
Authors: C Ross, ace, Andrei Barbu, Yevgeni Berzak, Battushig Myanganbayar, Boris Katz
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Language Acquisition, Semantic Parsing, Sentence

We develop a semantic parser that is trained in a grounded setting using pairs of videos captioned with sentences. This setting is both data-efficient, requiring little annotation, and similar to the experience of children where they observe their environment and listen to speakers. The semantic parser recovers the meaning of English sentences despite not having access to any annotated sentences. It does so despite the ambiguity inherent in vision where a sentence may refer to any combination of objects, object properties, relations or actions taken by any agent in a video. For this task, we collected a new dataset for grounded language acquisition. Learning a grounded semantic parser {---} turning sentences into logical forms using captioned videos {---} can significantly expand the range of data that parsers can be trained on, lower the effort of training a semantic parser, and ultimately lead to a better understanding of child language acquisition.
Deconvolutional Time Series Regression: A Technique for Modeling Temporally Diffuse Effects
Authors: Cory Shain, William Schuler
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: regression, Time Series, Time Series Analysis, Time Series Regression

Researchers in computational psycholinguistics frequently use linear models to study time series data generated by human subjects. However, time series may violate the assumptions of these models through temporal diffusion, where stimulus presentation has a lingering influence on the response as the rest of the experiment unfolds. This paper proposes a new statistical model that borrows from digital signal processing by recasting the predictors and response as convolutionally-related signals, using recent advances in machine learning to fit latent impulse response functions (IRFs) of arbitrary shape. A synthetic experiment shows successful recovery of true latent IRFs, and psycholinguistic experiments reveal plausible, replicable, and fine-grained estimates of latent temporal dynamics, with comparable or improved prediction quality to widely-used alternatives.
Is this Sentence Difficult? Do you Agree?
Authors: Dominique Brunato, Lorenzo De Mattei, Felice Dell{'}Orletta, Benedetta Iavarone, Giulia Venturi
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Language Acquisition, Sentence

In this paper, we present a crowdsourcing-based approach to model the human perception of sentence complexity. We collect a large corpus of sentences rated with judgments of complexity for two typologically-different languages, Italian and English. We test our approach in two experimental scenarios aimed to investigate the contribution of a wide set of lexical, morpho-syntactic and syntactic phenomena in predicting i) the degree of agreement among annotators independently from the assigned judgment and ii) the perception of sentence complexity.
Neural Transition Based Parsing of Web Queries: An Entity Based Approach
Authors: Rivka Malca, Roi Reichart
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Community Question Answering, Question Answering

Web queries with question intent manifest a complex syntactic structure and the processing of this structure is important for their interpretation. Pinter et al. (2016) has formalized the grammar of these queries and proposed semi-supervised algorithms for the adaptation of parsers originally designed to parse according to the standard dependency grammar, so that they can account for the unique forest grammar of queries. However, their algorithms rely on resources typically not available outside of big web corporates. We propose a new BiLSTM query parser that: (1) Explicitly accounts for the unique grammar of web queries; and (2) Utilizes named entity (NE) information from a BiLSTM NE tagger, that can be jointly trained with the parser. In order to train our model we annotate the query treebank of Pinter et al. (2016) with NEs. When trained on 2500 annotated queries our parser achieves UAS of 83.5{\%} and segmentation F1-score of 84.5, substantially outperforming existing state-of-the-art parsers.
Incremental Computation of Infix Probabilities for Probabilistic Finite Automata
Authors: Marco Cognetta, Yo-Sub Han, Soon Chan Kwon
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10

In natural language processing, a common task is to compute the probability of a phrase appearing in a document or to calculate the probability of all phrases matching a given pattern. For instance, one computes affix (prefix, suffix, infix, etc.) probabilities of a string or a set of strings with respect to a probability distribution of patterns. The problem of computing infix probabilities of strings when the pattern distribution is given by a probabilistic context-free grammar or by a probabilistic finite automaton is already solved, yet it was open to compute the infix probabilities in an incremental manner. The incremental computation is crucial when a new query is built from a previous query. We tackle this problem and suggest a method that computes infix probabilities incrementally for probabilistic finite automata by representing all the probabilities of matching strings as a series of transition matrix calculations. We show that the proposed approach is theoretically faster than the previous method and, using real world data, demonstrate that our approach has vastly better performance in practice.
Syntax Encoding with Application in Authorship Attribution
Authors: Richong Zhang, Zhiyuan Hu, Hongyu Guo, Yongyi Mao
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Authorship Attribution, Benchmarking, Feature Engineering, Sentence, Word Embeddings

We propose a novel strategy to encode the syntax parse tree of sentence into a learnable distributed representation. The proposed syntax encoding scheme is provably information-lossless. In specific, an embedding vector is constructed for each word in the sentence, encoding the path in the syntax tree corresponding to the word. The one-to-one correspondence between these {``}syntax-embedding{''} vectors and the words (hence their embedding vectors) in the sentence makes it easy to integrate such a representation with all word-level NLP models. We empirically show the benefits of the syntax embeddings on the Authorship Attribution domain, where our approach improves upon the prior art and achieves new performance records on five benchmarking data sets.
Sanskrit Word Segmentation Using Character-level Recurrent and Convolutional Neural Networks
Authors: Oliver Hellwig, Sebastian Nehrdich
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Feature Engineering

The paper introduces end-to-end neural network models that tokenize Sanskrit by jointly splitting compounds and resolving phonetic merges (Sandhi). Tokenization of Sanskrit depends on local phonetic and distant semantic features that are incorporated using convolutional and recurrent elements. Contrary to most previous systems, our models do not require feature engineering or extern linguistic resources, but operate solely on parallel versions of raw and segmented text. The models discussed in this paper clearly improve over previous approaches to Sanskrit word segmentation. As they are language agnostic, we will demonstrate that they also outperform the state of the art for the related task of German compound splitting.
Session-level Language Modeling for Conversational Speech
Authors: Wayne Xiong, Lingfeng Wu, Jun Zhang, Andreas Stolcke
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We propose to generalize language models for conversational speech recognition to allow them to operate across utterance boundaries and speaker changes, thereby capturing conversation-level phenomena such as adjacency pairs, lexical entrainment, and topical coherence. The model consists of a long-short-term memory (LSTM) recurrent network that reads the entire word-level history of a conversation, as well as information about turn taking and speaker overlap, in order to predict each next word. The model is applied in a rescoring framework, where the word history prior to the current utterance is approximated with preliminary recognition results. In experiments in the conversational telephone speech domain (Switchboard) we find that such a model gives substantial perplexity reductions over a standard LSTM-LM with utterance scope, as well as improvements in word error rate.
Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method
Authors: Yahui Liu, Wei Bi, Jun Gao, Xiaojiang Liu, Jian Yao, Shuming Shi
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dialogue Generation, Machine Translation, Short-Text Conversation

Sequence-to-sequence neural generation models have achieved promising performance on short text conversation tasks. However, they tend to generate generic/dull responses, leading to unsatisfying dialogue experience. We observe that in the conversation tasks, each query could have multiple responses, which forms a 1-to-n or m-to-n relationship in the view of the total corpus. The objective function used in standard sequence-to-sequence models will be dominated by loss terms with generic patterns. Inspired by this observation, we introduce a statistical re-weighting method that assigns different weights for the multiple responses of the same query, and trains the common neural generation model with the weights. Experimental results on a large Chinese dialogue corpus show that our method improves the acceptance rate of generated responses compared with several baseline models and significantly reduces the number of generated generic responses.
The glass ceiling in NLP
Authors: Natalie Schluter
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10

In this paper, we provide empirical evidence based on a rigourously studied mathematical model for bi-populated networks, that a glass ceiling within the field of NLP has developed since the mid 2000s.
Learning multiview embeddings for assessing dementia
Authors: Chlo{\'e} Pou-Prom, Frank Rudzicz
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: General Classification, regression

As the incidence of Alzheimer{'}s Disease (AD) increases, early detection becomes crucial. Unfortunately, datasets for AD assessment are often sparse and incomplete. In this work, we leverage the multiview nature of a small AD dataset, DementiaBank, to learn an embedding that captures different modes of cognitive impairment. We apply generalized canonical correlation analysis (GCCA) to our dataset and demonstrate the added benefit of using multiview embeddings in two downstream tasks: identifying AD and predicting clinical scores. By including multiview embeddings, we obtain an F1 score of 0.82 in the classification task and a mean absolute error of 3.42 in the regression task. Furthermore, we show that multiview embeddings can be obtained from other datasets as well.
Marginal Likelihood Training of BiLSTM-CRF for Biomedical Named Entity Recognition from Disjoint Label Sets
Authors: Nathan Greenberg, Trapit Bansal, Patrick Verga, Andrew McCallum
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Missing Labels, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Question Answering

Extracting typed entity mentions from text is a fundamental component to language understanding and reasoning. While there exist substantial labeled text datasets for multiple subsets of biomedical entity types{---}such as genes and proteins, or chemicals and diseases{---}it is rare to find large labeled datasets containing labels for all desired entity types together. This paper presents a method for training a single CRF extractor from multiple datasets with disjoint or partially overlapping sets of entity types. Our approach employs marginal likelihood training to insist on labels that are present in the data, while filling in {``}missing labels{''}. This allows us to leverage all the available data within a single model. In experimental results on the Biocreative V CDR (chemicals/diseases), Biocreative VI ChemProt (chemicals/proteins) and MedMentions (19 entity types) datasets, we show that joint training on multiple datasets improves NER F1 over training in isolation, and our methods achieve state-of-the-art results.
Deep Exhaustive Model for Nested Named Entity Recognition
Authors: Mohammad Golam Sohrab, Makoto Miwa
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Entity Linking, Feature Engineering, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Nested Named Entity Recognition, Relation Extraction

We propose a simple deep neural model for nested named entity recognition (NER). Most NER models focused on flat entities and ignored nested entities, which failed to fully capture underlying semantic information in texts. The key idea of our model is to enumerate all possible regions or spans as potential entity mentions and classify them with deep neural networks. To reduce the computational costs and capture the information of the contexts around the regions, the model represents the regions using the outputs of shared underlying bidirectional long short-term memory. We evaluate our exhaustive model on the GENIA and JNLPBA corpora in biomedical domain, and the results show that our model outperforms state-of-the-art models on nested and flat NER, achieving 77.1{\%} and 78.4{\%} respectively in terms of F-score, without any external knowledge resources.
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data
Authors: Wenhui Wang, Baobao Chang, Mairgup Mansur
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dependency Parsing, Feature Engineering, Language Modeling, Language Modelling, Machine Translation, Sentence, Word Embeddings

Pre-trained word embeddings and language model have been shown useful in a lot of tasks. However, both of them cannot directly capture word connections in a sentence, which is important for dependency parsing given its goal is to establish dependency relations between words. In this paper, we propose to implicitly capture word connections from unlabeled data by a word ordering model with self-attention mechanism. Experiments show that these implicit word connections do improve our parsing model. Furthermore, by combining with a pre-trained language model, our model gets state-of-the-art performance on the English PTB dataset, achieving 96.35{\%} UAS and 95.25{\%} LAS.
A Framework for Understanding the Role of Morphology in Universal Dependency Parsing
Authors: Mathieu Dehouck, Pascal Denis
Date: 2018-10-01 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dependency Parsing, Representation Learning, Word Embeddings

This paper presents a simple framework for characterizing morphological complexity and how it encodes syntactic information. In particular, we propose a new measure of morpho-syntactic complexity in terms of governor-dependent preferential attachment that explains parsing performance. Through experiments on dependency parsing with data from Universal Dependencies (UD), we show that representations derived from morphological attributes deliver important parsing performance improvements over standard word form embeddings when trained on the same datasets. We also show that the new morpho-syntactic complexity measure is predictive of the gains provided by using morphological attributes over plain forms on parsing scores, making it a tool to distinguish languages using morphology as a syntactic marker from others.