pwc-archive/papers-with-abstracts huggingface dataset:
Analyzing Well-Formedness of Syllables in Japanese Sign Language
Authors: Satoshi Yawata, Makoto Miwa, Yutaka Sasaki, Daisuke Hara
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Active Learning

This paper tackles a problem of analyzing the well-formedness of syllables in Japanese Sign Language (JSL). We formulate the problem as a classification problem that classifies syllables into well-formed or ill-formed. We build a data set that contains hand-coded syllables and their well-formedness. We define a fine-grained feature set based on the hand-coded syllables and train a logistic regression classifier on labeled syllables, expecting to find the discriminative features from the trained classifier. We also perform pseudo active learning to investigate the applicability of active learning in analyzing syllables. In the experiments, the best classifier with our combinatorial features achieved the accuracy of 87.0{\%}. The pseudo active learning is also shown to be effective showing that it could reduce about 84{\%} of training instances to achieve the accuracy of 82.0{\%} when compared to the model without active learning.
Towards Lower Bounds on Number of Dimensions for Word Embeddings
Authors: Kevin Patel, Pushpak Bhattacharyya
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Named Entity Recognition (NER), Part-Of-Speech Tagging, Sarcasm Detection, Sentence Classification, Sentiment Analysis, Word Embeddings

Word embeddings are a relatively new addition to the modern NLP researcher{'}s toolkit. However, unlike other tools, word embeddings are used in a black box manner. There are very few studies regarding various hyperparameters. One such hyperparameter is the dimension of word embeddings. They are rather decided based on a rule of thumb: in the range 50 to 300. In this paper, we show that the dimension should instead be chosen based on corpus statistics. More specifically, we show that the number of pairwise equidistant words of the corpus vocabulary (as defined by some distance/similarity metric) gives a lower bound on the the number of dimensions , and going below this bound results in degradation of quality of learned word embeddings. Through our evaluations on standard word embedding evaluation tasks, we show that for dimensions higher than or equal to the bound, we get better results as compared to the ones below it.
Post-Processing Techniques for Improving Predictions of Multilabel Learning Approaches
Authors: Akshay Soni, Aasish Pappu, Jerry Chia-mau Ni, Troy Chevalier
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11

In Multilabel Learning (MLL) each training instance is associated with a set of labels and the task is to learn a function that maps an unseen instance to its corresponding label set. In this paper, we present a suite of {--} MLL algorithm independent {--} post-processing techniques that utilize the conditional and directional label-dependences in order to make the predictions from any MLL approach more coherent and precise. We solve constraint optimization problem over the output produced by any MLL approach and the result is a refined version of the input predicted label set. Using proposed techniques, we show absolute improvement of 3{\%} on English News and 10{\%} on Chinese E-commerce datasets for P@K metric.
Learning Kernels over Strings using Gaussian Processes
Authors: Daniel Beck, Trevor Cohn
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Emotion Recognition, Gaussian Processes, regression

Non-contiguous word sequences are widely known to be important in modelling natural language. However they not explicitly encoded in common text representations. In this work we propose a model for text processing using string kernels, capable of flexibly representing non-contiguous sequences. Specifically, we derive a vectorised version of the string kernel algorithm and their gradients, allowing efficient hyperparameter optimisation as part of a Gaussian Process framework. Experiments on synthetic data and text regression for emotion analysis show the promise of this technique.
Substring Frequency Features for Segmentation of Japanese Katakana Words with Unlabeled Corpora
Authors: Yoshinari Fujinuma, Alvin Grissom II
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Information Retrieval, Machine Translation, Segmentation

Word segmentation is crucial in natural language processing tasks for unsegmented languages. In Japanese, many out-of-vocabulary words appear in the phonetic syllabary katakana, making segmentation more difficult due to the lack of clues found in mixed script settings. In this paper, we propose a straightforward approach based on a variant of tf-idf and apply it to the problem of word segmentation in Japanese. Even though our method uses only an unlabeled corpus, experimental results show that it achieves performance comparable to existing methods that use manually labeled corpora. Furthermore, it improves performance of simple word segmentation models trained on a manually labeled corpus.
MONPA: Multi-objective Named-entity and Part-of-speech Annotator for Chinese using Recurrent Neural Network
Authors: Yu-Lun Hsieh, Yung-Chun Chang, Yi-Jie Huang, Shu-Hao Yeh, Chun-Hung Chen, Wen-Lian Hsu
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Part-Of-Speech Tagging, POS, POS Tagging, Segmentation, Sentence

Part-of-speech (POS) tagging and named entity recognition (NER) are crucial steps in natural language processing. In addition, the difficulty of word segmentation places additional burden on those who intend to deal with languages such as Chinese, and pipelined systems often suffer from error propagation. This work proposes an end-to-end model using character-based recurrent neural network (RNN) to jointly accomplish segmentation, POS tagging and NER of a Chinese sentence. Experiments on previous word segmentation and NER datasets show that a single model with the proposed architecture is comparable to those trained specifically for each task, and outperforms freely-available softwares. Moreover, we provide a web-based interface for the public to easily access this resource.
Recall is the Proper Evaluation Metric for Word Segmentation
Authors: Yan Shao, Christian Hardmeier, Joakim Nivre
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Information Retrieval, Machine Translation, Part-Of-Speech Tagging, Retrieval, Segmentation

We extensively analyse the correlations and drawbacks of conventionally employed evaluation metrics for word segmentation. Unlike in standard information retrieval, precision favours under-splitting systems and therefore can be misleading in word segmentation. Overall, based on both theoretical and experimental analysis, we propose that precision should be excluded from the standard evaluation metrics and that the evaluation score obtained by using only recall is sufficient and better correlated with the performance of word segmentation systems.
Segment-Level Neural Conditional Random Fields for Named Entity Recognition
Authors: Motoki Sato, Hiroyuki Shindo, Ikuya Yamada, Yuji Matsumoto
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Chunking, Morphological Tagging, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

We present Segment-level Neural CRF, which combines neural networks with a linear chain CRF for segment-level sequence modeling tasks such as named entity recognition (NER) and syntactic chunking. Our segment-level CRF can consider higher-order label dependencies compared with conventional word-level CRF. Since it is difficult to consider all possible variable length segments, our method uses segment lattice constructed from the word-level tagging model to reduce the search space. Performing experiments on NER and chunking, we demonstrate that our method outperforms conventional word-level CRF with neural networks.
Integrating Vision and Language Datasets to Measure Word Concreteness
Authors: Gitit Kehat, James Pustejovsky
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Image Captioning, Image Retrieval, Question Answering

We present and take advantage of the inherent visualizability properties of words in visual corpora (the textual components of vision-language datasets) to compute concreteness scores for words. Our simple method does not require hand-annotated concreteness score lists for training, and yields state-of-the-art results when evaluated against concreteness scores lists and previously derived scores, as well as when used for metaphor detection.
Semantic Features Based on Word Alignments for Estimating Quality of Text Simplification
Authors: Tomoyuki Kajiwara, Atsushi Fujita
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation, Reading Comprehension, Semantic Textual Similarity, Text Generation, Text Simplification, Word Embeddings

This paper examines the usefulness of semantic features based on word alignments for estimating the quality of text simplification. Specifically, we introduce seven types of alignment-based features computed on the basis of word embeddings and paraphrase lexicons. Through an empirical experiment using the QATS dataset, we confirm that we can achieve the state-of-the-art performance only with these features.
Injecting Word Embeddings with Another Language's Resource : An Application of Bilingual Embeddings
Authors: P, Prakhar ey, Vikram Pudi, Manish Shrivastava
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Learning Word Embeddings, Word Embeddings, Word Similarity

Word embeddings learned from text corpus can be improved by injecting knowledge from external resources, while at the same time also specializing them for similarity or relatedness. These knowledge resources (like WordNet, Paraphrase Database) may not exist for all languages. In this work we introduce a method to inject word embeddings of a language with knowledge resource of another language by leveraging bilingual embeddings. First we improve word embeddings of German, Italian, French and Spanish using resources of English and test them on variety of word similarity tasks. Then we demonstrate the utility of our method by creating improved embeddings for Urdu and Telugu languages using Hindi WordNet, beating the previously established baseline for Urdu.
Improving Black-box Speech Recognition using Semantic Parsing
Authors: Rodolfo Corona, Jesse Thomason, Raymond Mooney
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, Natural Language Understanding, Re-Ranking, Semantic Parsing, speech-recognition, Speech Recognition

Speech is a natural channel for human-computer interaction in robotics and consumer applications. Natural language understanding pipelines that start with speech can have trouble recovering from speech recognition errors. Black-box automatic speech recognition (ASR) systems, built for general purpose use, are unable to take advantage of in-domain language models that could otherwise ameliorate these errors. In this work, we present a method for re-ranking black-box ASR hypotheses using an in-domain language model and semantic parser trained for a particular task. Our re-ranking method significantly improves both transcription accuracy and semantic understanding over a state-of-the-art ASR{'}s vanilla output.
Natural Language Informs the Interpretation of Iconic Gestures: A Computational Approach
Authors: Ting Han, Julian Hough, David Schlangen
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Form, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

When giving descriptions, speakers often signify object shape or size with hand gestures. Such so-called {`}iconic{'} gestures represent their meaning through their relevance to referents in the verbal content, rather than having a conventional form. The gesture form on its own is often ambiguous, and the aspect of the referent that it highlights is constrained by what the language makes salient. We show how the verbal content guides gesture interpretation through a computational model that frames the task as a multi-label classification task that maps multimodal utterances to semantic categories, using annotated human-human data.
Modelling Representation Noise in Emotion Analysis using Gaussian Processes
Authors: Daniel Beck
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Emotion Recognition, Gaussian Processes, Opinion Mining, regression, Word Embeddings

Emotion Analysis is the task of modelling latent emotions present in natural language. Labelled datasets for this task are scarce so learning good input text representations is not trivial. Using averaged word embeddings is a simple way to leverage unlabelled corpora to build text representations but this approach can be prone to noise either coming from the embedding themselves or the averaging procedure. In this paper we propose a model for Emotion Analysis using Gaussian Processes and kernels that are better suitable for functions that exhibit noisy behaviour. Empirical evaluations in a emotion prediction task show that our model outperforms commonly used baselines for regression.
Are Manually Prepared Affective Lexicons Really Useful for Sentiment Analysis
Authors: Minglei Li, Qin Lu, Yunfei Long
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Semantic Textual Similarity, Sentiment Analysis

In this paper, we investigate the effectiveness of different affective lexicons through sentiment analysis of phrases. We examine how phrases can be represented through manually prepared lexicons, extended lexicons using computational methods, or word embedding. Comparative studies clearly show that word embedding using unsupervised distributional method outperforms manually prepared lexicons no matter what affective models are used in the lexicons. Our conclusion is that although different affective lexicons are cognitively backed by theories, they do not show any advantage over the automatically obtained word embedding.
MTNA: A Neural Multi-task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews
Authors: Wei Xue, Wubai Zhou, Tao Li, Qing Wang
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Extract Aspect, General Classification, Multi-Task Learning, Opinion Mining, Sentiment Analysis, Term Extraction, Text Classification

Online reviews are valuable resources not only for consumers to make decisions before purchase, but also for providers to get feedbacks for their services or commodities. In Aspect Based Sentiment Analysis (ABSA), it is critical to identify aspect categories and extract aspect terms from the sentences of user-generated reviews. However, the two tasks are often treated independently, even though they are closely related. Intuitively, the learned knowledge of one task should inform the other learning task. In this paper, we propose a multi-task learning model based on neural networks to solve them together. We demonstrate the improved performance of our multi-task learning model over the models trained separately on three public dataset released by SemEval workshops.
Can Discourse Relations be Identified Incrementally?
Authors: Frances Yung, Hiroshi Noji, Yuji Matsumoto
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Discourse Parsing, Language Modeling, Language Modelling, Semantic Role Labeling, Sentence, Speech Recognition

Humans process language word by word and construct partial linguistic structures on the fly before the end of the sentence is perceived. Inspired by this cognitive ability, incremental algorithms for natural language processing tasks have been proposed and demonstrated promising performance. For discourse relation (DR) parsing, however, it is not yet clear to what extent humans can recognize DRs incrementally, because the latent {`}nodes{'} of discourse structure can span clauses and sentences. To answer this question, this work investigates incrementality in discourse processing based on a corpus annotated with DR signals. We find that DRs are dominantly signaled at the boundary between the two constituent discourse units. The findings complement existing psycholinguistic theories on expectation in discourse processing and provide direction for incremental discourse parsing.
Diversifying Neural Conversation Model with Maximal Marginal Relevance
Authors: Yiping Song, Zhiliang Tian, Dongyan Zhao, Ming Zhang, Rui Yan
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Diversity, Document Summarization, Information Retrieval, model, Text Categorization

Neural conversation systems, typically using sequence-to-sequence (seq2seq) models, are showing promising progress recently. However, traditional seq2seq suffer from a severe weakness: during beam search decoding, they tend to rank universal replies at the top of the candidate list, resulting in the lack of diversity among candidate replies. Maximum Marginal Relevance (MMR) is a ranking algorithm that has been widely used for subset selection. In this paper, we propose the MMR-BS decoding method, which incorporates MMR into the beam search (BS) process of seq2seq. The MMR-BS method improves the diversity of generated replies without sacrificing their high relevance with the user-issued query. Experiments show that our proposed model achieves the best performance among other comparison methods.
Dialog for Language to Code
Authors: Shobhit Chaurasia, Raymond J. Mooney
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Code Generation

Generating computer code from natural language descriptions has been a long-standing problem. Prior work in this domain has restricted itself to generating code in one shot from a single description. To overcome this limitation, we propose a system that can engage users in a dialog to clarify their intent until it has all the information to produce correct code. To evaluate the efficacy of dialog in code generation, we focus on synthesizing conditional statements in the form of IFTTT recipes.
Using Analytic Scoring Rubrics in the Automatic Assessment of College-Level Summary Writing Tasks in L2
Authors: Tamara Sladoljev-Agejev, Jan {\v{S}}najder
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Reading Comprehension, regression

Assessing summaries is a demanding, yet useful task which provides valuable information on language competence, especially for second language learners. We consider automated scoring of college-level summary writing task in English as a second language (EL2). We adopt the Reading-for-Understanding (RU) cognitive framework, extended with the Reading-to-Write (RW) element, and use analytic scoring with six rubrics covering content and writing quality. We show that regression models with reference-based and linguistic features considerably outperform the baselines across all the rubrics. Moreover, we find interesting correlations between summary features and analytic rubrics, revealing the links between the RU and RW constructs.
A Statistical Framework for Product Description Generation
Authors: Jinpeng Wang, Yutai Hou, Jing Liu, Yunbo Cao, Chin-Yew Lin
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Attribute, Data-to-Text Generation, Text Generation

We present in this paper a statistical framework that generates accurate and fluent product description from product attributes. Specifically, after extracting templates and learning writing knowledge from attribute-description parallel data, we use the learned knowledge to decide what to say and how to say for product description generation. To evaluate accuracy and fluency for the generated descriptions, in addition to BLEU and Recall, we propose to measure what to say (in terms of attribute coverage) and to measure how to say (by attribute-specified generation) separately. Experimental results show that our framework is effective.
Automatic Text Summarization Using Reinforcement Learning with Embedding Features
Authors: Gyoung Ho Lee, Kong Joo Lee
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: ARC, Information Retrieval, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Text Summarization

An automatic text summarization system can automatically generate a short and brief summary that contains a main concept of an original document. In this work, we explore the advantages of simple embedding features in Reinforcement leaning approach to automatic text summarization tasks. In addition, we propose a novel deep learning network for estimating Q-values used in Reinforcement learning. We evaluate our model by using ROUGE scores with DUC 2001, 2002, Wikipedia, ACL-ARC data. Evaluation results show that our model is competitive with the previous models.
SSAS: Semantic Similarity for Abstractive Summarization
Authors: Raghuram Vadapalli, Litton J Kurisinkel, Manish Gupta, Vasudeva Varma
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Abstractive Text Summarization, Natural Language Inference, Semantic Similarity, Semantic Textual Similarity

Ideally a metric evaluating an abstract system summary should represent the extent to which the system-generated summary approximates the semantic inference conceived by the reader using a human-written reference summary. Most of the previous approaches relied upon word or syntactic sub-sequence overlap to evaluate system-generated summaries. Such metrics cannot evaluate the summary at semantic inference level. Through this work we introduce the metric of Semantic Similarity for Abstractive Summarization (SSAS), which leverages natural language inference and paraphrasing techniques to frame a novel approach to evaluate system summaries at semantic inference level. SSAS is based upon a weighted composition of quantities representing the level of agreement, contradiction, independence, paraphrasing, and optionally ROUGE score between a system-generated and a human-written summary.
Taking into account Inter-sentence Similarity for Update Summarization
Authors: Ma{\^a}li Mnasri, Ga{\"e}l de Chalendar, Olivier Ferret
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Document Summarization, Extractive Summarization, Multi-Document Summarization, Natural Language Inference, Sentence, Sentence Similarity, Topic Models

Following Gillick and Favre (2009), a lot of work about extractive summarization has modeled this task by associating two contrary constraints: one aims at maximizing the coverage of the summary with respect to its information content while the other represents its size limit. In this context, the notion of redundancy is only implicitly taken into account. In this article, we extend the framework defined by Gillick and Favre (2009) by examining how and to what extent integrating semantic sentence similarity into an update summarization system can improve its results. We show more precisely the impact of this strategy through evaluations performed on DUC 2007 and TAC 2008 and 2009 datasets.