pwc-archive/papers-with-abstracts huggingface dataset:
CWIG3G2 - Complex Word Identification Task across Three Text Genres and Two User Groups
Authors: Seid Muhie Yimam, Sanja {\v{S}}tajner, Martin Riedl, Chris Biemann
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Complex Word Identification, Lexical Simplification, Reading Comprehension

Complex word identification (CWI) is an important task in text accessibility. However, due to the scarcity of CWI datasets, previous studies have only addressed this problem on Wikipedia sentences and have solely taken into account the needs of non-native English speakers. We collect a new CWI dataset (CWIG3G2) covering three text genres News, WikiNews, and Wikipedia) annotated by both native and non-native English speakers. Unlike previous datasets, we cover single words, as well as complex phrases, and present them for judgment in a paragraph context. We present the first study on cross-genre and cross-group CWI, showing measurable influences in native language and genre types.
Generating Stylistically Consistent Dialog Responses with Transfer Learning
Authors: Reina Akama, Kazuaki Inada, Naoya Inoue, Sosuke Kobayashi, Kentaro Inui
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Response Generation, Transfer Learning

We propose a novel, data-driven, and stylistically consistent dialog response generation system. To create a user-friendly system, it is crucial to make generated responses not only appropriate but also stylistically consistent. For leaning both the properties effectively, our proposed framework has two training stages inspired by transfer learning. First, we train the model to generate appropriate responses, and then we ensure that the responses have a specific style. Experimental results demonstrate that the proposed method produces stylistically consistent responses while maintaining the appropriateness of the responses learned in a general domain.
Towards Abstractive Multi-Document Summarization Using Submodular Function-Based Framework, Sentence Compression and Merging
Authors: Yllias Chali, Moin Tanvee, Mir Tafseer Nayeem
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Abstractive Text Summarization, Document Summarization, Multi-Document Summarization, Query-focused Summarization, Sentence, Sentence Compression, Text Generation

We propose a submodular function-based summarization system which integrates three important measures namely importance, coverage, and non-redundancy to detect the important sentences for the summary. We design monotone and submodular functions which allow us to apply an efficient and scalable greedy algorithm to obtain informative and well-covered summaries. In addition, we integrate two abstraction-based methods namely sentence compression and merging for generating an abstractive sentence set. We design our summarization models for both generic and query-focused summarization. Experimental results on DUC-2004 and DUC-2007 datasets show that our generic and query-focused summarizers have outperformed the state-of-the-art summarization systems in terms of ROUGE-1 and ROUGE-2 recall and F-measure.
Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network
Authors: Lisheng Fu, Thien Huu Nguyen, Bonan Min, Ralph Grishman
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Domain Adaptation, Relation, Relation Extraction, Unsupervised Domain Adaptation

Relations are expressed in many domains such as newswire, weblogs and phone conversations. Trained on a source domain, a relation extractor{'}s performance degrades when applied to target domains other than the source. A common yet labor-intensive method for domain adaptation is to construct a target-domain-specific labeled dataset for adapting the extractor. In response, we present an unsupervised domain adaptation method which only requires labels from the source domain. Our method is a joint model consisting of a CNN-based relation classifier and a domain-adversarial classifier. The two components are optimized jointly to learn a domain-independent representation for prediction on the target domain. Our model outperforms the state-of-the-art on all three test domains of ACE 2005.
Lexical Simplification with the Deep Structured Similarity Model
Authors: Lis Pereira, Xiaodong Liu, John Lee
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Image Captioning, Learning Word Embeddings, Lexical Simplification, Machine Translation, model, Question Answering, Word Embeddings

We explore the application of a Deep Structured Similarity Model (DSSM) to ranking in lexical simplification. Our results show that the DSSM can effectively capture fine-grained features to perform semantic matching when ranking substitution candidates, outperforming the state-of-the-art on two standard datasets used for the task.
Proofread Sentence Generation as Multi-Task Learning with Editing Operation Prediction
Authors: Yuta Hitomi, Hideaki Tamori, Naoaki Okazaki, Kentaro Inui
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Articles, Grammatical Error Correction, Multi-Task Learning, Sentence

This paper explores the idea of robot editors, automated proofreaders that enable journalists to improve the quality of their articles. We propose a novel neural model of multi-task learning that both generates proofread sentences and predicts the editing operations required to rewrite the source sentences and create the proofread ones. The model is trained using logs of the revisions made professional editors revising draft newspaper articles written by journalists. Experiments demonstrate the effectiveness of our multi-task learning approach and the potential value of using revision logs for this task.
An Exploration of Data Augmentation and RNN Architectures for Question Ranking in Community Question Answering
Authors: Charles Chen, Razvan Bunescu
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: BIG-bench Machine Learning, Community Question Answering, Data Augmentation, Question Answering, Question Similarity

The automation of tasks in community question answering (cQA) is dominated by machine learning approaches, whose performance is often limited by the number of training examples. Starting from a neural sequence learning approach with attention, we explore the impact of two data augmentation techniques on question ranking performance: a method that swaps reference questions with their paraphrases, and training on examples automatically selected from external datasets. Both methods are shown to lead to substantial gains in accuracy over a strong baseline. Further improvements are obtained by changing the model architecture to mirror the structure seen in the data.
Deriving Consensus for Multi-Parallel Corpora: an English Bible Study
Authors: Patrick Xia, David Yarowsky
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation

What can you do with multiple noisy versions of the same text? We present a method which generates a single consensus between multi-parallel corpora. By maximizing a function of linguistic features between word pairs, we jointly learn a single corpus-wide multiway alignment: a consensus between 27 versions of the English Bible. We additionally produce English paraphrases, word-level distributions of tags, and consensus dependency parses. Our method is language independent and applicable to any multi-parallel corpora. Given the Bible{'}s unique role as alignable bitext for over 800 of the world{'}s languages, this consensus alignment and resulting resources offer value for multilingual annotation projection, and also shed potential insights into the Bible itself.
MASSAlign: Alignment and Annotation of Comparable Documents
Authors: Gustavo Paetzold, Fern Alva-Manchego, o, Lucia Specia
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Sentence

We introduce MASSAlign: a Python library for the alignment and annotation of monolingual comparable documents. MASSAlign offers easy-to-use access to state of the art algorithms for paragraph and sentence-level alignment, as well as novel algorithms for word-level annotation of transformation operations between aligned sentences. In addition, MASSAlign provides a visualization module to display and analyze the alignments and annotations performed.
CADET: Computer Assisted Discovery Extraction and Translation
Authors: Benjamin Van Durme, Tom Lippincott, Kevin Duh, Deana Burchfield, Adam Poliak, Cash Costello, Tim Finin, Scott Miller, James Mayfield, Philipp Koehn, Craig Harman, Dawn Lawrie, Ch May, ler, Max Thomas, Annabelle Carrell, Julianne Chaloux, Tongfei Chen, Alex Comerford, Mark Dredze, Benjamin Glass, Shudong Hao, Patrick Martin, Pushpendre Rastogi, Rashmi Sankepally, Travis Wolfe, Ying-Ying Tran, Ted Zhang
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Active Learning, Machine Translation, Translation

Computer Assisted Discovery Extraction and Translation (CADET) is a workbench for helping knowledge workers find, label, and translate documents of interest. It combines a multitude of analytics together with a flexible environment for customizing the workflow for different users. This open-source framework allows for easy development of new research prototypes using a micro-service architecture based atop Docker and Apache Thrift.
WiseReporter: A Korean Report Generation System
Authors: Yunseok Noh, Su Jeong Choi, Seong-Bae Park, Se-Young Park
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Slot Filling, Text Generation, Text Summarization

We demonstrate a report generation system called WiseReporter. The WiseReporter generates a text report of a specific topic which is usually given as a keyword by verbalizing knowledge base facts involving the topic. This demonstration does not demonstate only the report itself, but also the processes how the sentences for the report are generated. We are planning to enhance WiseReporter in the future by adding data analysis based on deep learning architecture and text summarization.
Encyclolink: A Cross-Encyclopedia,Cross-language Article-Linking System and Web-based Search Interface
Authors: Yu-Chun Wang, Ka Ming Wong, Chun-Kai Wu, Chao-Lin Pan, Richard Tzong-Han Tsai
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Articles, Information Retrieval

Cross-language article linking (CLAL) is the task of finding corresponding article pairs across encyclopedias of different languages. In this paper, we present Encyclolink, a web-based CLAL search interface designed to help users find equivalent encyclopedia articles in Baidu Baike for a given English Wikipedia article title query. Encyclolink is powered by our cross-encyclopedia entity embedding CLAL system (0.8 MRR). The browser-based Interface provides users with a clear and easily readable preview of the contents of retrieved articles for comparison.
A Telecom-Domain Online Customer Service Assistant Based on Question Answering with Word Embedding and Intent Classification
Authors: Jui-Yang Wang, Min-Feng Kuo, Jen-Chieh Han, Chao-Chuang Shih, Chun-Hsun Chen, Po-Ching Lee, Richard Tzong-Han Tsai
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: General Classification, Information Retrieval, intent-classification, Intent Classification, Question Answering, Retrieval

In the paper, we propose an information retrieval based (IR-based) Question Answering (QA) system to assist online customer service staffs respond users in the telecom domain. When user asks a question, the system retrieves a set of relevant answers and ranks them. Moreover, our system uses a novel reranker to enhance the ranking result of information retrieval.It employs the word2vec model to represent the sentences as vectors. It also uses a sub-category feature, predicted by the k-nearest neighbor algorithm. Finally, the system returns the top five candidate answers, making online staffs find answers much more efficiently.
TOTEMSS: Topic-based, Temporal Sentiment Summarisation for Twitter
Authors: Bo Wang, Maria Liakata, Adam Tsakalidis, Spiros Georgakopoulos Kolaitis, Symeon Papadopoulos, Lazaros Apostolidis, Arkaitz Zubiaga, Rob Procter, Yiannis Kompatsiaris
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Opinion Mining, Sentiment Analysis

We present a system for time sensitive, topic based summarisation of the sentiment around target entities and topics in collections of tweets. We describe the main elements of the system and illustrate its functionality with two examples of sentiment analysis of topics related to the 2017 UK general election.
MUSST: A Multilingual Syntactic Simplification Tool
Authors: Carolina Scarton, Alessio Palmero Aprosio, Sara Tonelli, Tamara Mart{\'\i}n Wanton, Lucia Specia
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Lexical Simplification, Sentence, Text Simplification

We describe MUSST, a multilingual syntactic simplification tool. The tool supports sentence simplifications for English, Italian and Spanish, and can be easily extended to other languages. Our implementation includes a set of general-purpose simplification rules, as well as a sentence selection module (to select sentences to be simplified) and a confidence model (to select only promising simplifications). The tool was implemented in the context of the European project SIMPATICO on text simplification for Public Administration (PA) texts. Our evaluation on sentences in the PA domain shows that we obtain correct simplifications for 76{\%} of the simplified cases in English, 71{\%} of the cases in Spanish. For Italian, the results are lower (38{\%}) but the tool is still under development.
XMU Neural Machine Translation Online Service
Authors: Boli Wang, Zhixing Tan, Jinming Hu, Yidong Chen, Xiaodong Shi
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation, NMT, Translation

We demonstrate a neural machine translation web service. Our NMT service provides web-based translation interfaces for a variety of language pairs. We describe the architecture of NMT runtime pipeline and the training details of NMT models. We also show several applications of our online translation interfaces.
Semantics-Enhanced Task-Oriented Dialogue Translation: A Case Study on Hotel Booking
Authors: Long-Yue Wang, Jinhua Du, Liangyou Li, Zhaopeng Tu, Andy Way, Qun Liu
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Dialogue Understanding, Machine Translation, Management, Named Entity Recognition (NER), Translation

We showcase TODAY, a semantics-enhanced task-oriented dialogue translation system, whose novelties are: (i) task-oriented named entity (NE) definition and a hybrid strategy for NE recognition and translation; and (ii) a novel grounded semantic method for dialogue understanding and task-order management. TODAY is a case-study demo which can efficiently and accurately assist customers and agents in different languages to reach an agreement in a dialogue for the hotel booking.
ClassifierGuesser: A Context-based Classifier Prediction System for Chinese Language Learners
Authors: Nicole Peinelt, Maria Liakata, Shu-Kai Hsieh
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: BIG-bench Machine Learning

Classifiers are function words that are used to express quantities in Chinese and are especially difficult for language learners. In contrast to previous studies, we argue that the choice of classifiers is highly contextual and train context-aware machine learning models based on a novel publicly available dataset, outperforming previous baselines. We further present use cases for our database and models in an interactive demo system.
Automatic Difficulty Assessment for Chinese Texts
Authors: John Lee, Meichun Liu, Chun Yin Lam, Tak On Lau, Bing Li, Keying Li
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Dependency Parsing, Language Acquisition, Part-Of-Speech Tagging

We present a web-based interface that automatically assesses reading difficulty of Chinese texts. The system performs word segmentation, part-of-speech tagging and dependency parsing on the input text, and then determines the difficulty levels of the vocabulary items and grammatical constructions in the text. Furthermore, the system highlights the words and phrases that must be simplified or re-written in order to conform to the user-specified target difficulty level. Evaluation results show that the system accurately identifies the vocabulary level of 89.9{\%} of the words, and detects grammar points at 0.79 precision and 0.83 recall.
Verb Replacer: An English Verb Error Correction System
Authors: Yu-Hsuan Wu, Jhih-Jie Chen, Jason Chang
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: regression, Sentence

According to the analysis of Cambridge Learner Corpus, using a wrong verb is the most common type of grammatical errors. This paper describes Verb Replacer, a system for detecting and correcting potential verb errors in a given sentence. In our approach, alternative verbs are considered to replace the verb based on an error-annotated corpus and verb-object collocations. The method involves applying regression on channel models, parsing the sentence, identifying the verbs, retrieving a small set of alternative verbs, and evaluating each alternative. Our method combines and improves channel and language models, resulting in high recall of detecting and correcting verb misuse.
Learning Synchronous Grammar Patterns for Assisted Writing for Second Language Learners
Authors: Chi-En Wu, Jhih-Jie Chen, Jim Chang, Jason Chang
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: valid

In this paper, we present a method for extracting Synchronous Grammar Patterns (SGPs) from a given parallel corpus in order to assisted second language learners in writing. A grammar pattern consists of a head word (verb, noun, or adjective) and its syntactic environment. A synchronous grammar pattern describes a grammar pattern in the target language (e.g., English) and its counterpart in an other language (e.g., Mandarin), serving the purpose of native language support. Our method involves identifying the grammar patterns in the target language, aligning these patterns with the target language patterns, and finally filtering valid SGPs. The extracted SGPs with examples are then used to develop a prototype writing assistant system, called WriteAhead/bilingual. Evaluation on a set of randomly selected SGPs shows that our system provides satisfactory writing suggestions for English as a Second Language (ESL) learners.
Guess What: A Question Answering Game via On-demand Knowledge Validation
Authors: Yu-Sheng Li, Chien-Hui Tseng, Chian-Yun Huang, Wei-Yun Ma
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Chatbot, Question Answering

In this paper, we propose an idea of ondemand knowledge validation and fulfill the idea through an interactive Question-Answering (QA) game system, which is named Guess What. An object (e.g. dog) is first randomly chosen by the system, and then a user can repeatedly ask the system questions in natural language to guess what the object is. The system would respond with yes/no along with a confidence score. Some useful hints can also be given if needed. The proposed framework provides a pioneering example of on-demand knowledge validation in dialog environment to address such needs in AI agents/chatbots. Moreover, the released log data that the system gathered can be used to identify the most critical concepts/attributes of an existing knowledge base, which reflects human{'}s cognition about the world.
STCP: Simplified-Traditional Chinese Conversion and Proofreading
Authors: Jiarui Xu, Xuezhe Ma, Chen-Tse Tsai, Eduard Hovy
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11

This paper aims to provide an effective tool for conversion between Simplified Chinese and Traditional Chinese. We present STCP, a customizable system comprising statistical conversion model, and proofreading web interface. Experiments show that our system achieves comparable character-level conversion performance with the state-of-art systems. In addition, our proofreading interface can effectively support diagnostics and data annotation. STCP is available at \url{http://lagos.lti.cs.cmu.edu:8002/}
Deep Neural Network based system for solving Arithmetic Word problems
Authors: Purvanshi Mehta, Pruthwik Mishra, Vinayak Athavale, Manish Shrivastava, Dipti Sharma
Date: 2017-11-01 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Math

This paper presents DILTON a system which solves simple arithmetic word problems. DILTON uses a Deep Neural based model to solve math word problems. DILTON divides the question into two parts - worldstate and query. The worldstate and the query are processed separately in two different networks and finally, the networks are merged to predict the final operation. We report the first deep learning approach for the prediction of operation between two numbers. DILTON learns to predict operations with 88.81{\%} accuracy in a corpus of primary school questions.
IJCNLP-2017 Task 1: Chinese Grammatical Error Diagnosis
Authors: Gaoqi Rao, Baolin Zhang, Endong Xun, Lung-Hao Lee
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Grammatical Error Correction

This paper presents the IJCNLP 2017 shared task for Chinese grammatical error diagnosis (CGED) which seeks to identify grammatical error types and their range of occurrence within sentences written by learners of Chinese as foreign language. We describe the task definition, data preparation, performance metrics, and evaluation results. Of the 13 teams registered for this shared task, 5 teams developed the system and submitted a total of 13 runs. We expected this evaluation campaign could lead to the development of more advanced NLP techniques for educational applications, especially for Chinese error detection. All data sets with gold standards and scoring scripts are made publicly available to researchers.
IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases
Authors: Liang-Chih Yu, Lung-Hao Lee, Jin Wang, Kam-Fai Wong
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Sentiment Analysis, Task 2

This paper presents the IJCNLP 2017 shared task on Dimensional Sentiment Analysis for Chinese Phrases (DSAP) which seeks to identify a real-value sentiment score of Chinese single words and multi-word phrases in the both valence and arousal dimensions. Valence represents the degree of pleasant and unpleasant (or positive and negative) feelings, and arousal represents the degree of excitement and calm. Of the 19 teams registered for this shared task for two-dimensional sentiment analysis, 13 submitted results. We expected that this evaluation campaign could produce more advanced dimensional sentiment analysis techniques, especially for Chinese affective computing. All data sets with gold standards and scoring script are made publicly available to researchers.
IJCNLP-2017 Task 3: Review Opinion Diversification (RevOpiD-2017)
Authors: Anil Kumar Singh, Avijit Thawani, Mayank Panchal, Anubhav Gupta, Julian McAuley
Date: 2017-12-01 00:00:00
Proceeding: IJCNLP 2017 12
Tasks: Document Ranking, Document Summarization, Entity Disambiguation, Information Retrieval, Opinion Mining

Unlike Entity Disambiguation in web search results, Opinion Disambiguation is a relatively unexplored topic. RevOpiD shared task at IJCNLP-2107 aimed to attract attention towards this research problem. In this paper, we summarize the first run of this task and introduce a new dataset that we have annotated for the purpose of evaluating Opinion Mining, Summarization and Disambiguation methods.