pwc-archive/papers-with-abstracts huggingface dataset:
Multilingual Information Extraction with PolyglotIE
Authors: Alan Akbik, Laura Chiticariu, Marina Danilevsky, Yonas Kbrom, Yunyao Li, Huaiyu Zhu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Semantic Parsing

We present PolyglotIE, a web-based tool for developing extractors that perform Information Extraction (IE) over multilingual data. Our tool has two core features: First, it allows users to develop extractors against a unified abstraction that is shared across a large set of natural languages. This means that an extractor needs only be created once for one language, but will then run on multilingual data without any additional effort or language-specific knowledge on part of the user. Second, it embeds this abstraction as a set of views within a declarative IE system, allowing users to quickly create extractors using a mature IE query language. We present PolyglotIE as a hands-on demo in which users can experiment with creating extractors, execute them on multilingual text and inspect extraction results. Using the UI, we discuss the challenges and potential of using unified, crosslingual semantic abstractions as basis for downstream applications. We demonstrate multilingual IE for 9 languages from 4 different language groups: English, German, French, Spanish, Japanese, Chinese, Arabic, Russian and Hindi.
WordForce: Visualizing Controversial Words in Debates
Authors: Wei-Fan Chen, Fang-Yu Lin, Lun-Wei Ku
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Sentiment Analysis, Word Embeddings

This paper presents WordForce, a system powered by the state of the art neural network model to visualize the learned user-dependent word embeddings from each post according to the post content and its engaged users. It generates the scatter plots to show the force of a word, i.e., whether the semantics of word embeddings from posts of different stances are clearly separated from the aspect of this controversial word. In addition, WordForce provides the dispersion and the distance of word embeddings from posts of different stance groups, and proposes the most controversial words accordingly to show clues to what people argue about in a debate.
Zara: A Virtual Interactive Dialogue System Incorporating Emotion, Sentiment and Personality Recognition
Authors: Pascale Fung, Anik Dey, Farhad Bin Siddique, Ruixi Lin, Yang Yang, Dario Bertero, Yan Wan, Ricky Ho Yin Chan, Chien-Sheng Wu
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Emotion Recognition, Feature Engineering, Sentiment Analysis, speech-recognition, Speech Recognition

Zara, or {`}Zara the Supergirl{'} is a virtual robot, that can exhibit empathy while interacting with an user, with the aid of its built in facial and emotion recognition, sentiment analysis, and speech module. At the end of the 5-10 minute conversation, Zara can give a personality analysis of the user based on all the user utterances. We have also implemented a real-time emotion recognition, using a CNN model that detects emotion from raw audio without feature extraction, and have achieved an average of 65.7{\%} accuracy on six different emotion classes, which is an impressive 4.5{\%} improvement from the conventional feature based SVM classification. Also, we have described a CNN based sentiment analysis module trained using out-of-domain data, that recognizes sentiment from the speech recognition transcript, which has a 74.8 F-measure when tested on human-machine dialogues.
NL2KB: Resolving Vocabulary Gap between Natural Language and Knowledge Base in Knowledge Base Construction and Retrieval
Authors: Sheng-Lun Wei, Yen-Pin Chiu, Hen-Hsen Huang, Hsin-Hsi Chen
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Knowledge Base Construction, Question Answering, Relation Extraction, Retrieval

Words to express relations in natural language (NL) statements may be different from those to represent properties in knowledge bases (KB). The vocabulary gap becomes barriers for knowledge base construction and retrieval. With the demo system called NL2KB in this paper, users can browse which properties in KB side may be mapped to for a given relational pattern in NL side. Besides, they can retrieve the sets of relational patterns in NL side for a given property in KB side. We describe how the mapping is established in detail. Although the mined patterns are used for Chinese knowledge base applications, the methodology can be extended to other languages.
PKUSUMSUM : A Java Platform for Multilingual Document Summarization
Authors: Jianmin Zhang, Tianming Wang, Xiaojun Wan
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Chinese Word Segmentation, Document Summarization, Information Retrieval

PKUSUMSUM is a Java platform for multilingual document summarization, and it sup-ports multiple languages, integrates 10 automatic summarization methods, and tackles three typical summarization tasks. The summarization platform has been released and users can easily use and update it. In this paper, we make a brief description of the char-acteristics, the summarization methods, and the evaluation results of the platform, and al-so compare PKUSUMSUM with other summarization toolkits.
Kotonush: Understanding Concepts Based on Values behind Social Media
Authors: Tatsuya Iwanari, Kohei Ohara, Naoki Yoshinaga, Nobuhiro Kaji, Masashi Toyoda, Masaru Kitsuregawa
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Aspect-Based Sentiment Analysis (ABSA), Attribute, Sentiment Analysis

Kotonush, a system that clarifies people{'}s values on various concepts on the basis of what they write about on social media, is presented. The values are represented by ordering sets of concepts (e.g., London, Berlin, and Rome) in accordance with a common attribute intensity expressed by an adjective (e.g., entertaining). We exploit social media text written by different demographics and at different times in order to induce specific orderings for comparison. The system combines a text-to-ordering module with an interactive querying interface enabled by massive hyponymy relations and provides mechanisms to compare the induced orderings from various viewpoints. We empirically evaluate Kotonush and present some case studies, featuring real-world concept orderings with different domains on Twitter, to demonstrate the usefulness of our system.
Exploring a Continuous and Flexible Representation of the Lexicon
Authors: Pierre Marchal, Thierry Poibeau
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Navigate, Semantic Textual Similarity

We aim at showing that lexical descriptions based on multifactorial and continuous models can be used by linguists and lexicographers (and not only by machines) so long as they are provided with a way to efficiently navigate data collections. We propose to demonstrate such a system.
Automatically Suggesting Example Sentences of Near-Synonyms for Language Learners
Authors: Chieh-Yang Huang, Nicole Peinelt, Lun-Wei Ku
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12

In this paper, we propose GiveMeExample that ranks example sentences according to their capacity of demonstrating the differences among English and Chinese near-synonyms for language learners. The difficulty of the example sentences is automatically detected. Furthermore, the usage models of the near-synonyms are built by the GMM and Bi-LSTM models to suggest the best elaborative sentences. Experiments show the good performance both in the fill-in-the-blank test and on the manually labeled gold data, that is, the built models can select the appropriate words for the given context and vice versa.
Kyoto-NMT: a Neural Machine Translation implementation in Chainer
Authors: Fabien Cromi{\`e}res
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Deep Learning, Language Modeling, Language Modelling, Machine Translation, NMT, Translation

We present Kyoto-NMT, an open-source implementation of the Neural Machine Translation paradigm. This implementation is done in Python and Chainer, an easy-to-use Deep Learning Framework.
Compositional Distributional Models of Meaning
Authors: Mehrnoosh Sadrzadeh, Dimitri Kartsaklis
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Machine Translation, Natural Language Inference, Sentence, Sentiment Analysis

Compositional distributional models of meaning (CDMs) provide a function that produces a vectorial representation for a phrase or a sentence by composing the vectors of its words. Being the natural evolution of the traditional and well-studied distributional models at the word level, CDMs are steadily evolving to a popular and active area of NLP. This COLING 2016 tutorial aims at providing a concise introduction to this emerging field, presenting the different classes of CDMs and the various issues related to them in sufficient detail.
Natural Language Processing for Intelligent Access to Scientific Information
Authors: Horacio Saggion, Francesco Ronzano
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Articles, Natural Language Inference, Question Answering

During the last decade the amount of scientific information available on-line increased at an unprecedented rate. As a consequence, nowadays researchers are overwhelmed by an enormous and continuously growing number of articles to consider when they perform research activities like the exploration of advances in specific topics, peer reviewing, writing and evaluation of proposals. Natural Language Processing Technology represents a key enabling factor in providing scientists with intelligent patterns to access to scientific information. Extracting information from scientific papers, for example, can contribute to the development of rich scientific knowledge bases which can be leveraged to support intelligent knowledge access and question answering. Summarization techniques can reduce the size of long papers to their essential content or automatically generate state-of-the-art-reviews. Paraphrase or textual entailment techniques can contribute to the identification of relations across different scientific textual sources. This tutorial provides an overview of the most relevant tasks related to the processing of scientific documents, including but not limited to the in-depth analysis of the structure of the scientific articles, their semantic interpretation, content extraction and summarization.
Quality Estimation for Language Output Applications
Authors: Carolina Scarton, Gustavo Paetzold, Lucia Specia
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: BIG-bench Machine Learning, Machine Translation, Multi-Task Learning, Text Simplification, Translation

Quality Estimation (QE) of language output applications is a research area that has been attracting significant attention. The goal of QE is to estimate the quality of language output applications without the need of human references. Instead, machine learning algorithms are used to build supervised models based on a few labelled training instances. Such models are able to generalise over unseen data and thus QE is a robust method applicable to scenarios where human input is not available or possible. One such a scenario where QE is particularly appealing is that of Machine Translation, where a score for predicted quality can help decide whether or not a translation is useful (e.g. for post-editing) or reliable (e.g. for gisting). Other potential applications within Natural Language Processing (NLP) include Text Summarisation and Text Simplification. In this tutorial we present the task of QE and its application in NLP, focusing on Machine Translation. We also introduce QuEst++, a toolkit for QE that encompasses feature extraction and machine learning, and propose a practical activity to extend this toolkit in various ways.
Translationese: Between Human and Machine Translation
Authors: Shuly Wintner
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Language Identification, Machine Translation, Text Classification, Translation

Translated texts, in any language, have unique characteristics that set them apart from texts originally written in the same language. Translation Studies is a research field that focuses on investigating these characteristics. Until recently, research in machine translation (MT) has been entirely divorced from translation studies. The main goal of this tutorial is to introduce some of the findings of translation studies to researchers interested mainly in machine translation, and to demonstrate that awareness to these findings can result in better, more accurate MT systems.
Succinct Data Structures for NLP-at-Scale
Authors: Matthias Petri, Trevor Cohn
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Information Retrieval, Language Modelling, Retrieval

Succinct data structures involve the use of novel data structures, compression technologies, and other mechanisms to allow data to be stored in extremely small memory or disk footprints, while still allowing for efficient access to the underlying data. They have successfully been applied in areas such as Information Retrieval and Bioinformatics to create highly compressible in-memory search indexes which provide efficient search functionality over datasets which traditionally could only be processed using external memory data structures. Modern technologies in this space are not well known within the NLP community, but have the potential to revolutionise NLP, particularly the application to {`}big data{'} in the form of terabyte and larger corpora. This tutorial will present a practical introduction to the most important succinct data structures, tools, and applications with the intent of providing the researchers with a jump-start into this domain. The focus of this tutorial will be efficient text processing utilising space efficient representations of suffix arrays, suffix trees and searchable integer compression schemes with specific applications of succinct data structures to common NLP tasks such as $n$-gram language modelling.
The Role of Wikipedia in Text Analysis and Retrieval
Authors: Marius Pa{\c{s}}ca
Date: 2016-12-01 00:00:00
Proceeding: COLING 2016 12
Tasks: Coreference Resolution, Information Retrieval, Retrieval

This tutorial examines the characteristics, advantages and limitations of Wikipedia relative to other existing, human-curated resources of knowledge; derivative resources, created by converting semi-structured content in Wikipedia into structured data; the role of Wikipedia and its derivatives in text analysis; and the role of Wikipedia and its derivatives in enhancing information retrieval.
Context-Aware Smoothing for Neural Machine Translation
Authors: Kehai Chen, Rui Wang, Masao Utiyama, Eiichiro Sumita, Tiejun Zhao
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation, NMT, Representation Learning, Sentence, Translation

In Neural Machine Translation (NMT), each word is represented as a low-dimension, real-value vector for encoding its syntax and semantic information. This means that even if the word is in a different sentence context, it is represented as the fixed vector to learn source representation. Moreover, a large number of Out-Of-Vocabulary (OOV) words, which have different syntax and semantic information, are represented as the same vector representation of {``}unk{''}. To alleviate this problem, we propose a novel context-aware smoothing method to dynamically learn a sentence-specific vector for each word (including OOV words) depending on its local context words in a sentence. The learned context-aware representation is integrated into the NMT to improve the translation performance. Empirical results on NIST Chinese-to-English translation task show that the proposed approach achieves 1.78 BLEU improvements on average over a strong attentional NMT, and outperforms some existing systems.
Improving Sequence to Sequence Neural Machine Translation by Utilizing Syntactic Dependency Information
Authors: An Nguyen Le, Ander Martinez, Akifumi Yoshimoto, Yuji Matsumoto
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Decoder, Machine Translation, Translation

Sequence to Sequence Neural Machine Translation has achieved significant performance in recent years. Yet, there are some existing issues that Neural Machine Translation still does not solve completely. Two of them are translation for long sentences and the {``}over-translation{''}. To address these two problems, we propose an approach that utilize more grammatical information such as syntactic dependencies, so that the output can be generated based on more abundant information. In our approach, syntactic dependencies is employed in decoding. In addition, the output of the model is presented not as a simple sequence of tokens but as a linearized tree construction. In order to assess the performance, we construct model based on an attention mechanism encoder-decoder model in which the source language is input to the encoder as a sequence and the decoder generates the target language as a linearized dependency tree structure. Experiments on the Europarl-v7 dataset of French-to-English translation demonstrate that our proposed method improves BLEU scores by 1.57 and 2.40 on datasets consisting of sentences with up to 50 and 80 tokens, respectively. Furthermore, the proposed method also solved the two existing problems, ineffective translation for long sentences and over-translation in Neural Machine Translation.
Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings
Authors: Masahiro Kaneko, Yuya Sakaizawa, Mamoru Komachi
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Grammatical Error Detection, Learning Word Embeddings, Word Embeddings

In this study, we improve grammatical error detection by learning word embeddings that consider grammaticality and error patterns. Most existing algorithms for learning word embeddings usually model only the syntactic context of words so that classifiers treat erroneous and correct words as similar inputs. We address the problem of contextual information by considering learner errors. Specifically, we propose two models: one model that employs grammatical error patterns and another model that considers grammaticality of the target word. We determine grammaticality of n-gram sequence from the annotated error tags and extract grammatical error patterns for word embeddings from large-scale learner corpora. Experimental results show that a bidirectional long-short term memory model initialized by our word embeddings achieved the state-of-the-art accuracy by a large margin in an English grammatical error detection task on the First Certificate in English dataset.
Dependency Parsing with Partial Annotations: An Empirical Comparison
Authors: Yue Zhang, Zhenghua Li, Jun Lang, Qingrong Xia, Min Zhang
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Active Learning, Dependency Parsing

This paper describes and compares two straightforward approaches for dependency parsing with partial annotations (PA). The first approach is based on a forest-based training objective for two CRF parsers, i.e., a biaffine neural network graph-based parser (Biaffine) and a traditional log-linear graph-based parser (LLGPar). The second approach is based on the idea of constrained decoding for three parsers, i.e., a traditional linear graph-based parser (LGPar), a globally normalized neural network transition-based parser (GN3Par) and a traditional linear transition-based parser (LTPar). For the test phase, constrained decoding is also used for completing partial trees. We conduct experiments on Penn Treebank under three different settings for simulating PA, i.e., random, most uncertain, and divergent outputs from the five parsers. The results show that LLGPar is most effective in directly learning from PA, and other parsers can achieve best performance when PAs are completed into full trees by LLGPar.
Word Ordering as Unsupervised Learning Towards Syntactically Plausible Word Representations
Authors: Noriki Nishida, Hideki Nakayama
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Dependency Parsing, Part-Of-Speech Tagging, Word Embeddings

The research question we explore in this study is how to obtain syntactically plausible word representations without using human annotations. Our underlying hypothesis is that word ordering tests, or linearizations, is suitable for learning syntactic knowledge about words. To verify this hypothesis, we develop a differentiable model called Word Ordering Network (WON) that explicitly learns to recover correct word order while implicitly acquiring word embeddings representing syntactic knowledge. We evaluate the word embeddings produced by the proposed method on downstream syntax-related tasks such as part-of-speech tagging and dependency parsing. The experimental results demonstrate that the WON consistently outperforms both order-insensitive and order-sensitive baselines on these tasks.
MIPA: Mutual Information Based Paraphrase Acquisition via Bilingual Pivoting
Authors: Tomoyuki Kajiwara, Mamoru Komachi, Daichi Mochihashi
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Learning Word Embeddings, Semantic Textual Similarity, Word Alignment, Word Embeddings

We present a pointwise mutual information (PMI)-based approach to formalize paraphrasability and propose a variant of PMI, called MIPA, for the paraphrase acquisition. Our paraphrase acquisition method first acquires lexical paraphrase pairs by bilingual pivoting and then reranks them by PMI and distributional similarity. The complementary nature of information from bilingual corpora and from monolingual corpora makes the proposed method robust. Experimental results show that the proposed method substantially outperforms bilingual pivoting and distributional similarity themselves in terms of metrics such as MRR, MAP, coverage, and Spearman{'}s correlation.
Enabling Transitivity for Lexical Inference on Chinese Verbs Using Probabilistic Soft Logic
Authors: Wei-Chung Wang, Lun-Wei Ku
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Natural Language Inference, Open-Domain Question Answering, Question Answering, Text Generation

To learn more knowledge, enabling transitivity is a vital step for lexical inference. However, most of the lexical inference models with good performance are for nouns or noun phrases, which cannot be directly applied to the inference on events or states. In this paper, we construct the largest Chinese verb lexical inference dataset containing 18,029 verb pairs, where for each pair one of four inference relations are annotated. We further build a probabilistic soft logic (PSL) model to infer verb lexicons using the logic language. With PSL, we easily enable transitivity in two layers, the observed layer and the feature layer, which are included in the knowledge base. We further discuss the effect of transitives within and between these layers. Results show the performance of the proposed PSL model can be improved at least 3.5{\%} (relative) when the transitivity is enabled. Furthermore, experiments show that enabling transitivity in the observed layer benefits the most.