pwc-archive/papers-with-abstracts huggingface dataset:
Assessment of an Index for Measuring Pronunciation Difficulty
Authors: Katsunori Kotani, Takehiko Yoshimi
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Sentence, valid

This study assesses an index for measur-ing the pronunciation difficulty of sen-tences (henceforth, pronounceability) based on the normalized edit distance from a reference sentence to a transcrip-tion of learners{'} pronunciation. Pro-nounceability should be examined when language teachers use a computer-assisted language learning system for pronunciation learning to maintain the motivation of learners. However, unlike the evaluation of learners{'} pronunciation performance, previous research did not focus on pronounceability not only for English but also for Asian languages. This study found that the normalized edit distance was reliable but not valid. The lack of validity appeared to be because of an English test used for determining the proficiency of learners.
A Short Answer Grading System in Chinese by Support Vector Approach
Authors: Shih-Hung Wu, Wen-Feng Shih
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: BIG-bench Machine Learning, Semantic Textual Similarity

In this paper, we report a short answer grading system in Chinese. We build a system based on standard machine learning approaches and test it with translated corpus from two publicly available corpus in English. The experiment results show similar results on two different corpus as in English.
From Fidelity to Fluency: Natural Language Processing for Translator Training
Authors: Oi Yee Kwong
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Navigate, Translation

This study explores the use of natural language processing techniques to enhance bilingual lexical access beyond simple equivalents, to enable translators to navigate along a wider cross-lingual lexical space and more examples showing different translation strategies, which is essential for them to learn to produce not only faithful but also fluent translations.
Measuring Beginner Friendliness of Japanese Web Pages explaining Academic Concepts by Integrating Neural Image Feature and Text Features
Authors: Hayato Shiokawa, Kota Kawaguchi, Bingcai Han, Takehito Utsuro, Yasuhide Kawada, Masaharu Yoshioka, K, Noriko o
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7

Search engine is an important tool of modern academic study, but the results are lack of measurement of beginner friendliness. In order to improve the efficiency of using search engine for academic study, it is necessary to invent a technique of measuring the beginner friendliness of a Web page explaining academic concepts and to build an automatic measurement system. This paper studies how to integrate heterogeneous features such as a neural image feature generated from the image of the Web page by a variant of CNN (convolutional neural network) as well as text features extracted from the body text of the HTML file of the Web page. Integration is performed through the framework of the SVM classifier learning. Evaluation results show that heterogeneous features perform better than each individual type of features.
Multilingual Short Text Responses Clustering for Mobile Educational Activities: a Preliminary Exploration
Authors: Yuen-Hsien Tseng, Lung-Hao Lee, Yu-Ta Chien, Chun-Yen Chang, Tsung-Yen Li
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Clustering, Short Text Clustering, Text Clustering

Text clustering is a powerful technique to detect topics from document corpora, so as to provide information browsing, analysis, and organization. On the other hand, the Instant Response System (IRS) has been widely used in recent years to enhance student engagement in class and thus improve their learning effectiveness. However, the lack of functions to process short text responses from the IRS prevents the further application of IRS in classes. Therefore, this study aims to propose a proper short text clustering module for the IRS, and demonstrate our implemented techniques through real-world examples, so as to provide experiences and insights for further study. In particular, we have compared three clustering methods and the result shows that theoretically better methods need not lead to better results, as there are various factors that may affect the final performance.
Chinese Grammatical Error Diagnosis Based on CRF and LSTM-CRF model
Authors: Yujie Zhou, Yinan Shao, Yong Zhou
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Feature Engineering

When learning Chinese as a foreign language, the learners may have some grammatical errors due to negative migration of their native languages. However, few grammar checking applications have been developed to support the learners. The goal of this paper is to develop a tool to automatically diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W) in Chinese sentences written by those foreign learners. In this paper, a conventional linear CRF model with specific feature engineering and a LSTM-CRF model are used to solve the CGED (Chinese Grammatical Error Diagnosis) task. We make some improvement on both models and the submitted results have better performance on false positive rate and accuracy than the average of all runs from CGED2018 for all three evaluation levels.
Contextualized Character Representation for Chinese Grammatical Error Diagnosis
Authors: Jianbo Zhao, Si Li, Zhiqing Lin
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7

Nowadays, more and more people are learning Chinese as their second language. Establishing an automatic diagnosis system for Chinese grammatical error has become an important challenge. In this paper, we propose a Chinese grammatical error diagnosis (CGED) model with contextualized character representation. Compared to the traditional model using LSTM (Long-Short Term Memory), our model have better performance and there is no need to add too many artificial features.
CMMC-BDRC Solution to the NLP-TEA-2018 Chinese Grammatical Error Diagnosis Task
Authors: Yongwei Zhang, Qinan Hu, Fang Liu, Yueguo Gu
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Data Augmentation, General Classification, Position, text-classification, Text Classification

Chinese grammatical error diagnosis is an important natural language processing (NLP) task, which is also an important application using artificial intelligence technology in language education. This paper introduces a system developed by the Chinese Multilingual {\&} Multimodal Corpus and Big Data Research Center for the NLP-TEA shared task, named Chinese Grammar Error Diagnosis (CGED). This system regards diagnosing errors task as a sequence tagging problem, while takes correction task as a text classification problem. Finally, in the 12 teams, this system gets the highest F1 score in the detection task and the second highest F1 score in mean in the identification task, position task and the correction task.
Detecting Simultaneously Chinese Grammar Errors Based on a BiLSTM-CRF Model
Authors: Yajun Liu, Hong-ying Zan, Mengjie Zhong, Hongchao Ma
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Position

In the process of learning and using Chinese, many learners of Chinese as foreign language(CFL) may have grammar errors due to negative migration of their native languages. This paper introduces our system that can simultaneously diagnose four types of grammatical errors including redundant (R), missing (M), selection (S), disorder (W) in NLPTEA-5 shared task. We proposed a Bidirectional LSTM CRF neural network (BiLSTM-CRF) that combines BiLSTM and CRF without hand-craft features for Chinese Grammatical Error Diagnosis (CGED). Evaluation includes three levels, which are detection level, identification level and position level. At the detection level and identification level, our system got the third recall scores, and achieved good F1 values.
A Hybrid Approach Combining Statistical Knowledge with Conditional Random Fields for Chinese Grammatical Error Detection
Authors: Yiyi Wang, Chilin Shih
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Grammatical Error Detection

This paper presents a method of combining Conditional Random Fields (CRFs) model with a post-processing layer using Google n-grams statistical information tailored to detect word selection and word order errors made by learners of Chinese as Foreign Language (CFL). We describe the architecture of the model and its performance in the shared task of the ACL 2018 Workshop on Natural Language Processing Techniques for Educational Applications (NLPTEA). This hybrid approach yields comparably high false positive rate (FPR = 0.1274) and precision (Pd= 0.7519; Pi= 0.6311), but low recall (Rd = 0.3035; Ri = 0.1696 ) in grammatical error detection and identification tasks. Additional statistical information and linguistic rules can be added to enhance the model performance in the future.
CYUT-III Team Chinese Grammatical Error Diagnosis System Report in NLPTEA-2018 CGED Shared Task
Authors: Shih-Hung Wu, Jun-Wei Wang, Liang-Pu Chen, Ping-Che Yang
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Language Modeling, Language Modelling

This paper reports how we build a Chinese Grammatical Error Diagnosis system in the NLPTEA-2018 CGED shared task. In 2018, we sent three runs with three different approaches. The first one is a pattern-based approach by frequent error pattern matching. The second one is a sequential labelling approach by conditional random fields (CRF). The third one is a rewriting approach by sequence to sequence (seq2seq) model. The three approaches have different properties that aim to optimize different performance metrics and the formal run results show the differences as we expected.
Detecting Grammatical Errors in the NTOU CGED System by Identifying Frequent Subsentences
Authors: Chuan-Jie Lin, Shao-Heng Chen
Date: 2018-07-01 00:00:00
Proceeding: WS 2018 7
Tasks: Sentence

The main goal of Chinese grammatical error diagnosis task is to detect word er-rors in the sentences written by Chinese-learning students. Our previous system would generate error-corrected sentences as candidates and their sentence likeli-hood were measured based on a large scale Chinese n-gram dataset. This year we further tried to identify long frequent-ly-seen subsentences and label them as correct in order to avoid propose too many error candidates. Two new methods for suggesting missing and selection er-rors were also tested.
Corpus Phonetics: Past, Present, and Future
Authors: Mark Liberman
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

Invited talk
Using Linguistic Resources to Evaluate the Quality of Annotated Corpora
Authors: Max Silberztein
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

Statistical and neural-network-based methods that compute their results by comparing a given text to be analyzed with a reference corpus assume that the reference corpus is complete and reliable enough. In this article, I conduct several experiments on an extract of the Open American National Corpus to verify this assumption.
Rule-based vs. Neural Net Approaches to Semantic Textual Similarity
Authors: Linrui Zhang, Dan Moldovan
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Feature Engineering, Semantic Textual Similarity, Sentence Pair Modeling, STS

This paper presents a neural net approach to determine Semantic Textual Similarity (STS) using attention-based bidirectional Long Short-Term Memory Networks (Bi-LSTM). To this date, most of the traditional STS systems were rule-based that built on top of excessive use of linguistic features and resources. In this paper, we present an end-to-end attention-based Bi-LSTM neural network system that solely takes word-level features, without expensive feature engineering work or the usage of external resources. By comparing its performance with traditional rule-based systems against SemEval-2012 benchmark, we make an assessment on the limitations and strengths of neural net systems to rule-based systems on Semantic Textual Similarity.
Linguistic Resources for Phrasal Verb Identification
Authors: Peter Machonis
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: All

This paper shows how a Lexicon-Grammar dictionary of English phrasal verbs (PV) can be transformed into an electronic dictionary, and with the help of multiple grammars, dictionaries, and filters within the linguistic development environment, NooJ, how to accurately identify PV in large corpora. The NooJ program is an alternative to statistical methods commonly used in NLP: all PV are listed in a dictionary and then located by means of a PV grammar in both continuous and discontinuous format. Results are then refined with a series of dictionaries, disambiguating grammars, and other linguistics recourses. The main advantage of such a program is that all PV can be identified in any corpus. The only drawback is that PV not listed in the dictionary (e.g., archaic forms, recent neologisms) are not identified; however, new PV can easily be added to the electronic dictionary, which is freely available to all.
Designing a Croatian Aspectual Derivatives Dictionary: Preliminary Stages
Authors: Kristina Kocijan, Kre{\v{s}}imir {\v{S}}ojat, Dario Poljak
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

The paper focusses on derivationally connected verbs in Croatian, i.e. on verbs that share the same lexical morpheme and are derived from other verbs via prefixation, suffixation and/or stem alternations. As in other Slavic languages with rich derivational morphology, each verb is marked for aspect, either perfective or imperfective. Some verbs, mostly of foreign origin, are marked as bi-aspectual verbs. The main objective of this paper is to detect and to describe major derivational processes and affixes used in the derivation of aspectually connected verbs with NooJ. Annotated chains are exported into a format adequate for web database system and further used to enhance the aspectual and derivational information for each verb.
A Rule-Based System for Disambiguating French Locative Verbs and Their Translation into Arabic
Authors: Safa Boudhina, H{\'e}la Fehri
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Form, Machine Translation, Translation, Word Sense Disambiguation

This paper presents a rule-based system for disambiguating frensh locative verbs and their translation to Arabic language. The disambiguation phase is based on the use of the French Verb dictionary (LVF) of Dubois and Dubois Charlier as a linguistic resource, from which a base of disambiguation rules is extracted. The extracted rules thus take the form of transducers which will be subsequently applied to texts. The translation phase consists in translating the disambiguated locative verbs returned by the disambiguation phase. The translation takes into account the verb{'}s tense used as well as the inflected form of the verb. This phase is based on bilingual dictionaries that contain the different French locative verbs and their translation into the Arabic language. The experimentation and the evaluation are done in the linguistic platform NooJ. The obtained results are satisfactory.
A Pedagogical Application of NooJ in Language Teaching: The Adjective in Spanish and Italian
Authors: Andrea Rodrigo, Mario Monteleone, Silvia Reyes
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

In this paper, a pedagogical application of NooJ to the teaching and learning of Spanish as a foreign language is presented, which is directed to a specific addressee: learners whose mother tongue is Italian. The category {`}adjective{'} has been chosen on account of its lower frequency of occurrence in texts written in Spanish, and particularly in the Argentine Rioplatense variety, and with the aim of developing strategies to increase its use. In addition, the features that the adjective shares with other grammatical categories render it extremely productive and provide elements that enrich the learners{'} proficiency. The reference corpus contains the front pages of the Argentinian newspaper Clar{\'\i}n related to an emblematic historical moment, whose starting point is 24 March 1976, when a military coup began, and covers a thirty year period until 24 March 2006. It can be seen how the term desaparecido emerges with all its cultural and social charge, providing a context which allows an approach to Rioplatense Spanish from a more comprehensive perspective. Finally, a pedagogical proposal accounting for the application of the NooJ platform in language teaching is included.
STYLUS: A Resource for Systematically Derived Language Usage
Authors: Bonnie Dorr, Clare Voss
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Dialogue Management, Robot Navigation, Text Generation

We describe a resource derived through extraction of a set of argument realizations from an existing lexical-conceptual structure (LCS) Verb Database of 500 verb classes (containing a total of 9525 verb entries) to include information about realization of arguments for a range of different verb classes. We demonstrate that our extended resource, called STYLUS (SysTematicallY Derived Language USe), enables systematic derivation of regular patterns of language usage without requiring manual annotation. We posit that both spatially oriented applications such as robot navigation and more general applications such as narrative generation require a layered representation scheme where a set of primitives (often grounded in space/motion such as GO) is coupled with a representation of constraints at the syntax-semantics interface. We demonstrate that the resulting resource covers three cases of lexico-semantic operations applicable to both language understanding and language generation.
Gold Corpus for Telegraphic Summarization
Authors: Chanakya Malireddy, Srivenkata N M Somisetty, Manish Shrivastava
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Articles, Extractive Summarization

Most extractive summarization techniques operate by ranking all the source sentences and then select the top ranked sentences as the summary. Such methods are known to produce good summaries, especially when applied to news articles and scientific texts. However, they don{'}t fare so well when applied to texts such as fictional narratives, which don{'}t have a single central or recurrent theme. This is because usually the information or plot of the story is spread across several sentences. In this paper, we discuss a different summarization technique called Telegraphic Summarization. Here, we don{'}t select whole sentences, rather pick short segments of text spread across sentences, as the summary. We have tailored a set of guidelines to create such summaries and, using the same, annotate a gold corpus of 200 English short stories.
Design of a Tigrinya Language Speech Corpus for Speech Recognition
Authors: Hafte Abera, Sebsibe H/mariam
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: speech-recognition, Speech Recognition

In this paper, we describe the first Tigrinya Languages speech corpora designed and development for speech recognition purposes. Tigrinya, often written as Tigrigna (ትግርኛ) /tɪˈɡrinjə/ belongs to the Semitic branch of the Afro-Asiatic languages where it shows the characteristic features of a Semitic language. It is spoken by ethnic Tigray-Tigrigna people in the Horn of Africa. The paper outlines different corpus designing process analysis of related work on speech corpora creation for different languages. The authors provide also procedures that were used for the creation of Tigrinya speech recognition corpus which is the under-resourced language. One hundred and thirty speakers, native to Tigrinya language, were recorded for training and test dataset set. Each speaker read 100 texts, which consisted of syllabically rich and balanced sentences. Ten thousand sets of sentences were used to prompt sheets. These sentences contained all of the contextual syllables and phones.
Parallel Corpora for bi-Directional Statistical Machine Translation for Seven Ethiopian Language Pairs
Authors: Solomon Teferra Abate, Michael Melese, Martha Yifiru Tachbelie, Million Meshesha, Solomon Atinafu, Wondwossen Mulugeta, Yaregal Assabie, Hafte Abera, Binyam Ephrem, Tewodros Abebe, Wondimagegnhue Tsegaye, Amanuel Lemma, Tsegaye Andargie, Seifedin Shifaw
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Translation

In this paper, we describe the development of parallel corpora for Ethiopian Languages: Amharic, Tigrigna, Afan-Oromo, Wolaytta and Geez. To check the usability of all the corpora we conducted baseline bi-directional statistical machine translation (SMT) experiments for seven language pairs. The performance of the bi-directional SMT systems shows that all the corpora can be used for further investigations. We have also shown that the morphological complexity of the Ethio-Semitic languages has a negative impact on the performance of the SMT especially when they are target languages. Based on the results we obtained, we are currently working towards handling the morphological complexities to improve the performance of statistical machine translation among the Ethiopian languages.
Using Embeddings to Compare FrameNet Frames Across Languages
Authors: Jennifer Sikos, Sebastian Pad{\'o}
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8

Much interest in Frame Semantics is fueled by the substantial extent of its applicability across languages. At the same time, lexicographic studies have found that the applicability of individual frames can be diminished by cross-lingual divergences regarding polysemy, syntactic valency, and lexicalization. Due to the large effort involved in manual investigations, there are so far no broad-coverage resources with {``}problematic{''} frames for any language pair. Our study investigates to what extent multilingual vector representations of frames learned from manually annotated corpora can address this need by serving as a wide coverage source for such divergences. We present a case study for the language pair English {---} German using the FrameNet and SALSA corpora and find that inferences can be made about cross-lingual frame applicability using a vector space model.
Construction of a Multilingual Corpus Annotated with Translation Relations
Authors: Yuming Zhai, Aur{\'e}lien Max, Anne Vilnat
Date: 2018-08-01 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, Paraphrase Generation, Translation

Translation relations, which distinguish literal translation from other translation techniques, constitute an important subject of study for human translators (Chuquet and Paillard, 1989). However, automatic processing techniques based on interlingual relations, such as machine translation or paraphrase generation exploiting translational equivalence, have not exploited these relations explicitly until now. In this work, we present a categorisation of translation relations and annotate them in a parallel multilingual (English, French, Chinese) corpus of oral presentations, the TED Talks. Our long term objective will be to automatically detect these relations in order to integrate them as important characteristics for the search of monolingual segments in relation of equivalence (paraphrases) or of entailment. The annotated corpus resulting from our work will be made available to the community.