pwc-archive/papers-with-abstracts huggingface dataset:
Hindi Shabdamitra: A Wordnet based E-Learning Tool for Language Learning and Teaching
Authors: Hanumant Redkar, S. Singh, hya, Meenakshi Somasundaram, Dhara Gorasia, Malhar Kulkarni, Pushpak Bhattacharyya
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12
Tasks: Self-Learning

In today{'}s technology driven digital era, education domain is undergoing a transformation from traditional approaches to more learner controlled and flexible methods of learning. This transformation has opened the new avenues for interdisciplinary research in the field of educational technology and natural language processing in developing quality digital aids for learning and teaching. The tool presented here - Hindi Shabhadamitra, developed using Hindi Wordnet for Hindi language learning, is one such e-learning tool. It has been developed as a teaching and learning aid suitable for formal school based curriculum and informal setup for self learning users. Besides vocabulary, it also provides word based grammar along with images and pronunciation for better learning and retention. This aid demonstrates that how a rich lexical resource like wordnet can be systematically remodeled for practical usage in the educational domain.
NLPTEA 2017 Shared Task -- Chinese Spelling Check
Authors: Gabriel Fung, Maxime Debosschere, Dingmin Wang, Bo Li, Jia Zhu, Kam-Fai Wong
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12
Tasks: Sentence

This paper provides an overview along with our findings of the Chinese Spelling Check shared task at NLPTEA 2017. The goal of this task is to develop a computer-assisted system to automatically diagnose typing errors in traditional Chinese sentences written by students. We defined six types of errors which belong to two categories. Given a sentence, the system should detect where the errors are, and for each detected error determine its type and provide correction suggestions. We designed, constructed, and released a benchmark dataset for this task.
Chinese Spelling Check based on N-gram and String Matching Algorithm
Authors: Jui-Feng Yeh, Li-Ting Chang, Chan-Yi Liu, Tsung-Wei Hsu
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12
Tasks: Language Modeling, Language Modelling, Sentence, Spelling Correction

This paper presents a Chinese spelling check approach based on language models combined with string match algorithm to treat the problems resulted from the influence caused by Cantonese mother tone. N-grams first used to detecting the probability of sentence constructed by the writers, a string matching algorithm called Knuth-Morris-Pratt (KMP) Algorithm is used to detect and correct the error. According to the experimental results, the proposed approach can detect the error and provide the corresponding correction.
N-gram Model for Chinese Grammatical Error Diagnosis
Authors: Jianbo Zhao, Hao liu, Zuyi Bao, Xiaopeng Bai, Si Li, Zhiqing Lin
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12
Tasks: Language Modeling, Language Modelling, model

Detection and correction of Chinese grammatical errors have been two of major challenges for Chinese automatic grammatical error diagnosis.This paper presents an N-gram model for automatic detection and correction of Chinese grammatical errors in NLPTEA 2017 task. The experiment results show that the proposed method is good at correction of Chinese grammatical errors.
The Influence of Spelling Errors on Content Scoring Performance
Authors: Andrea Horbach, Yuning Ding, Torsten Zesch
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12
Tasks: BIG-bench Machine Learning

Spelling errors occur frequently in educational settings, but their influence on automatic scoring is largely unknown. We therefore investigate the influence of spelling errors on content scoring performance using the example of the ASAP corpus. We conduct an annotation study on the nature of spelling errors in the ASAP dataset and utilize these finding in machine learning experiments that measure the influence of spelling errors on automatic content scoring. Our main finding is that scoring methods using both token and character n-gram features are robust against spelling errors up to the error frequency in ASAP.
Analyzing the Impact of Spelling Errors on POS-Tagging and Chunking in Learner English
Authors: Tomoya Mizumoto, Ryo Nagata
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12
Tasks: Chunking, Grammatical Error Correction, Part-Of-Speech Tagging, POS, POS Tagging

Part-of-speech (POS) tagging and chunking have been used in tasks targeting learner English; however, to the best our knowledge, few studies have evaluated their performance and no studies have revealed the causes of POS-tagging/chunking errors in detail. Therefore, we investigate performance and analyze the causes of failure. We focus on spelling errors that occur frequently in learner English. We demonstrate that spelling errors reduced POS-tagging performance by 0.23{\%} owing to spelling errors, and that a spell checker is not necessary for POS-tagging/chunking of learner English.
Suggesting Sentences for ESL using Kernel Embeddings
Authors: Kent Shioda, Mamoru Komachi, Rue Ikeya, Daichi Mochihashi
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12
Tasks: Retrieval, Semantic Textual Similarity, Sentence, Sentence Retrieval

Sentence retrieval is an important NLP application for English as a Second Language (ESL) learners. ESL learners are familiar with web search engines, but generic web search results may not be adequate for composing documents in a specific domain. However, if we build our own search system specialized to a domain, it may be subject to the data sparseness problem. Recently proposed word2vec partially addresses the data sparseness problem, but fails to extract sentences relevant to queries owing to the modeling of the latent intent of the query. Thus, we propose a method of retrieving example sentences using kernel embeddings and N-gram windows. This method implicitly models latent intent of query and sentences, and alleviates the problem of noisy alignment. Our results show that our method achieved higher precision in sentence retrieval for ESL in the domain of a university press release corpus, as compared to a previous unsupervised method used for a semantic textual similarity task.
Event Timeline Generation from History Textbooks
Authors: Harsimran Bedi, Sangameshwar Patil, Swapnil Hingmire, Girish Palshikar
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12

Event timeline serves as the basic structure of history, and it is used as a disposition of key phenomena in studying history as a subject in secondary school. In order to enable a student to understand a historical phenomenon as a series of connected events, we present a system for automatic event timeline generation from history textbooks. Additionally, we propose Message Sequence Chart (MSC) and time-map based visualization techniques to visualize an event timeline. We also identify key computational challenges in developing natural language processing based applications for history textbooks.
Group Linguistic Bias Aware Neural Response Generation
Authors: Jianan Wang, Xin Wang, Fang Li, Zhen Xu, Zhuoran Wang, Baoxun Wang
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12
Tasks: Decoder, Response Generation

For practical chatbots, one of the essential factor for improving user experience is the capability of customizing the talking style of the agents, that is, to make chatbots provide responses meeting users{'} preference on language styles, topics, etc. To address this issue, this paper proposes to incorporate linguistic biases, which implicitly involved in the conversation corpora generated by human groups in the Social Network Services (SNS), into the encoder-decoder based response generator. By attaching a specially designed neural component to dynamically control the impact of linguistic biases in response generation, a Group Linguistic Bias Aware Neural Response Generation (GLBA-NRG) model is eventually presented. The experimental results on the dataset from the Chinese SNS show that the proposed architecture outperforms the current response generating models by producing both meaningful and vivid responses with customized styles.
Neural Regularized Domain Adaptation for Chinese Word Segmentation
Authors: Zuyi Bao, Si Li, Weiran Xu, Sheng Gao
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12
Tasks: Chinese Word Segmentation, Domain Adaptation, General Knowledge, Model Compression, Segmentation

For Chinese word segmentation, the large-scale annotated corpora mainly focus on newswire and only a handful of annotated data is available in other domains such as patents and literature. Considering the limited amount of annotated target domain data, it is a challenge for segmenters to learn domain-specific information while avoid getting over-fitted at the same time. In this paper, we propose a neural regularized domain adaptation method for Chinese word segmentation. The teacher networks trained in source domain are employed to regularize the training process of the student network by preserving the general knowledge. In the experiments, our neural regularized domain adaptation method achieves a better performance comparing to previous methods.
The Sentimental Value of Chinese Sub-Character Components
Authors: Yassine Benajiba, Or Biran, Zhiliang Weng, Yong Zhang, Jin Sun
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12
Tasks: Sentiment Analysis, Word Embeddings

Sub-character components of Chinese characters carry important semantic information, and recent studies have shown that utilizing this information can improve performance on core semantic tasks. In this paper, we hypothesize that in addition to semantic information, sub-character components may also carry emotional information, and that utilizing it should improve performance on sentiment analysis tasks. We conduct a series of experiments on four Chinese sentiment data sets and show that we can significantly improve the performance in various tasks over that of a character-level embeddings baseline. We then focus on qualitatively assessing multiple examples and trying to explain how the sub-character components affect the results in each case.
Chinese Answer Extraction Based on POS Tree and Genetic Algorithm
Authors: Shuihua Li, Xiao-Ming Zhang, Zhoujun Li
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12
Tasks: Information Retrieval, POS, Question Answering

Answer extraction is the most important part of a chinese web-based question answering system. In order to enhance the robustness and adaptability of answer extraction to new domains and eliminate the influence of the incomplete and noisy search snippets, we propose two new answer exraction methods. We utilize text patterns to generate Part-of-Speech (POS) patterns. In addition, a method is proposed to construct a POS tree by using these POS patterns. The POS tree is useful to candidate answer extraction of web-based question answering. To retrieve a efficient POS tree, the similarities between questions are used to select the question-answer pairs whose questions are similar to the unanswered question. Then, the POS tree is improved based on these question-answer pairs. In order to rank these candidate answers, the weights of the leaf nodes of the POS tree are calculated using a heuristic method. Moreover, the Genetic Algorithm (GA) is used to train the weights. The experimental results of 10-fold crossvalidation show that the weighted POS tree trained by GA can improve the accuracy of answer extraction.
Learning from Parenthetical Sentences for Term Translation in Machine Translation
Authors: Guoping Huang, Jiajun Zhang, Yu Zhou, Cheng-qing Zong
Date: 2017-12-01 00:00:00
Proceeding: WS 2017 12
Tasks: Machine Translation, Sentence, Translation

Terms extensively exist in specific domains, and term translation plays a critical role in domain-specific machine translation (MT) tasks. However, it{'}s a challenging task to translate them correctly for the huge number of pre-existing terms and the endless new terms. To achieve better term translation quality, it is necessary to inject external term knowledge into the underlying MT system. Fortunately, there are plenty of term translation knowledge in parenthetical sentences on the Internet. In this paper, we propose a simple, straightforward and effective framework to improve term translation by learning from parenthetical sentences. This framework includes: (1) a focused web crawler; (2) a parenthetical sentence filter, acquiring parenthetical sentences including bilingual term pairs; (3) a term translation knowledge extractor, extracting bilingual term translation candidates; (4) a probability learner, generating the term translation table for MT decoders. The extensive experiments demonstrate that our proposed framework significantly improves the translation quality of terms and sentences.
Automatically Acquired Lexical Knowledge Improves Japanese Joint Morphological and Dependency Analysis
Authors: Daisuke Kawahara, Yuta Hayashibe, Hajime Morita, Sadao Kurohashi
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Lemmatization, Morphological Analysis, POS, Segmentation

This paper presents a joint model for morphological and dependency analysis based on automatically acquired lexical knowledge. This model takes advantage of rich lexical knowledge to simultaneously resolve word segmentation, POS, and dependency ambiguities. In our experiments on Japanese, we show the effectiveness of our joint model over conventional pipeline models.
Lexicalized vs. Delexicalized Parsing in Low-Resource Scenarios
Authors: Agnieszka Falenska, {\"O}zlem {\c{C}}etino{\u{g}}lu
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Dependency Parsing, Word Embeddings

We present a systematic analysis of lexicalized vs. delexicalized parsing in low-resource scenarios, and propose a methodology to choose one method over another under certain conditions. We create a set of simulation experiments on 41 languages and apply our findings to 9 low-resource languages. Experimental results show that our methodology chooses the best approach in 8 out of 9 cases.
Improving neural tagging with lexical information
Authors: Beno{\^\i}t Sagot, H{\'e}ctor Mart{\'\i}nez Alonso
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Part-Of-Speech Tagging, Word Embeddings

Neural part-of-speech tagging has achieved competitive results with the incorporation of character-based and pre-trained word embeddings. In this paper, we show that a state-of-the-art bi-LSTM tagger can benefit from using information from morphosyntactic lexicons as additional input. The tagger, trained on several dozen languages, shows a consistent, average improvement when using lexical information, even when also using character-based embeddings, thus showing the complementarity of the different sources of lexical information. The improvements are particularly important for the smaller datasets.
Prepositional Phrase Attachment over Word Embedding Products
Authors: Pranava Swaroop Madhyastha, Xavier Carreras, Ariadna Quattoni
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Dependency Parsing, Prepositional Phrase Attachment, Word Embeddings

We present a low-rank multi-linear model for the task of solving prepositional phrase attachment ambiguity (PP task). Our model exploits tensor products of word embeddings, capturing all possible conjunctions of latent embeddings. Our results on a wide range of datasets and task settings show that tensor products are the best compositional operation and that a relatively simple multi-linear model that uses only word embeddings of lexical features can outperform more complex non-linear architectures that exploit the same information. Our proposed model gives the current best reported performance on an out-of-domain evaluation and performs competively on out-of-domain dependency parsing datasets.
L1-L2 Parallel Dependency Treebank as Learner Corpus
Authors: John Lee, Keying Li, Herman Leung
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Retrieval

This opinion paper proposes the use of parallel treebank as learner corpus. We show how an L1-L2 parallel treebank {---} i.e., parse trees of non-native sentences, aligned to the parse trees of their target hypotheses {---} can facilitate retrieval of sentences with specific learner errors. We argue for its benefits, in terms of corpus re-use and interoperability, over a conventional learner corpus annotated with error tags. As a proof of concept, we conduct a case study on word-order errors made by learners of Chinese as a foreign language. We report precision and recall in retrieving a range of word-order error categories from L1-L2 tree pairs annotated in the Universal Dependency framework.
Splitting Complex English Sentences
Authors: John Lee, J. Buddhika K. Pathirage Don
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Lexical Simplification, Machine Translation, POS, Reading Comprehension, Sentence, Text Simplification

This paper applies parsing technology to the task of syntactic simplification of English sentences, focusing on the identification of text spans that can be removed from a complex sentence. We report the most comprehensive evaluation to-date on this task, using a dataset of sentences that exhibit simplification based on coordination, subordination, punctuation/parataxis, adjectival clauses, participial phrases, and appositive phrases. We train a decision tree with features derived from text span length, POS tags and dependency relations, and show that it significantly outperforms a parser-only baseline.
Hierarchical Word Structure-based Parsing: A Feasibility Study on UD-style Dependency Parsing in Japanese
Authors: Takaaki Tanaka, Katsuhiko Hayashi, Masaaki Nagata
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Chunking, Dependency Parsing, Morphological Analysis, Sentence

In applying word-based dependency parsing such as Universal Dependencies (UD) to Japanese, the uncertainty of word segmentation emerges for defining a word unit of the dependencies. We introduce the following hierarchical word structures to dependency parsing in Japanese: morphological units (a short unit word, SUW) and syntactic units (a long unit word, LUW). An SUW can be used to segment a sentence consistently, while it is too short to represent syntactic construction. An LUW is a unit including functional multiwords and LUW-based analysis facilitates the capturing of syntactic structure and makes parsing results more precise than SUW-based analysis. This paper describes the results of a feasibility study on the ability and the effectiveness of parsing methods based on hierarchical word structure (LUW chunking+parsing) in comparison to single layer word structure (SUW parsing). We also show joint analysis of LUW-chunking and dependency parsing improves the performance of identifying predicate-argument structures, while there is not much difference between overall results of them. not much difference between overall results of them.
Leveraging Newswire Treebanks for Parsing Conversational Data with Argument Scrambling
Authors: Riyaz A. Bhat, Irshad Bhat, Dipti Sharma
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9

We investigate the problem of parsing conversational data of morphologically-rich languages such as Hindi where argument scrambling occurs frequently. We evaluate a state-of-the-art non-linear transition-based parsing system on a new dataset containing 506 dependency trees for sentences from Bollywood (Hindi) movie scripts and Twitter posts of Hindi monolingual speakers. We show that a dependency parser trained on a newswire treebank is strongly biased towards the canonical structures and degrades when applied to conversational data. Inspired by Transformational Generative Grammar (Chomsky, 1965), we mitigate the sampling bias by generating all theoretically possible alternative word orders of a clause from the existing (kernel) structures in the treebank. Training our parser on canonical and transformed structures improves performance on conversational data by around 9{\%} LAS over the baseline newswire parser.
Using hyperlinks to improve multilingual partial parsers
Authors: Anders S{\o}gaard
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Machine Translation, Speech Synthesis

Syntactic annotation is costly and not available for the vast majority of the world{'}s languages. We show that sometimes we can do away with less labeled data by exploiting more readily available forms of mark-up. Specifically, we revisit an idea from Valentin Spitkovsky{'}s work (2010), namely that hyperlinks typically bracket syntactic constituents or chunks. We strengthen his results by showing that not only can hyperlinks help in low resource scenarios, exemplified here by Quechua, but learning from hyperlinks can also improve state-of-the-art NLP models for English newswire. We also present out-of-domain evaluation on English Ontonotes 4.0.
Correcting prepositional phrase attachments using multimodal corpora
Authors: Sebastien Delecraz, Alexis Nasr, Frederic Bechet, Benoit Favre
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Prepositional Phrase Attachment, Text Generation

PP-attachments are an important source of errors in parsing natural language. We propose in this article to use data coming from a multimodal corpus, combining textual, visual and conceptual information, as well as a correction strategy, to propose alternative attachments in the output of a parser.
Exploiting Structure in Parsing to 1-Endpoint-Crossing Graphs
Authors: Robin Kurtz, Marco Kuhlmann
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: Dependency Parsing, Sentence

Deep dependency parsing can be cast as the search for maximum acyclic subgraphs in weighted digraphs. Because this search problem is intractable in the general case, we consider its restriction to the class of 1-endpoint-crossing (1ec) graphs, which has high coverage on standard data sets. Our main contribution is a characterization of 1ec graphs as a subclass of the graphs with pagenumber at most 3. Building on this we show how to extend an existing parsing algorithm for 1-endpoint-crossing trees to the full class. While the runtime complexity of the extended algorithm is polynomial in the length of the input sentence, it features a large constant, which poses a challenge for practical implementations.
Effective Online Reordering with Arc-Eager Transitions
Authors: Ryosuke Kohita, Hiroshi Noji, Yuji Matsumoto
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: ARC, Dependency Parsing, Transition-Based Dependency Parsing

We present a new transition system with word reordering for unrestricted non-projective dependency parsing. Our system is based on decomposed arc-eager rather than arc-standard, which allows more flexible ambiguity resolution between a local projective and non-local crossing attachment. In our experiment on Universal Dependencies 2.0, we find our parser outperforms the ordinary swap-based parser particularly on languages with a large amount of non-projectivity.
Arc-Hybrid Non-Projective Dependency Parsing with a Static-Dynamic Oracle
Authors: Miryam de Lhoneux, Sara Stymne, Joakim Nivre
Date: 2017-09-01 00:00:00
Proceeding: WS 2017 9
Tasks: ARC, Dependency Parsing

In this paper, we extend the arc-hybrid system for transition-based parsing with a swap transition that enables reordering of the words and construction of non-projective trees. Although this extension breaks the arc-decomposability of the transition system, we show how the existing dynamic oracle for this system can be modified and combined with a static oracle only for the swap transition. Experiments on 5 languages show that the new system gives competitive accuracy and is significantly better than a system trained with a purely static oracle.