Paper Group NANR 215
DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach. ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling. An Empirical Evaluation of Noise Contrastive Estimation for the Neural Network Joint Model of Translation. Generating Video Description …
DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach
| Title | DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach |
| Authors | V{'\i}ctor Martinez Morant, LLu{'\i}s-F. Hurtado, Ferran Pla |
| Abstract | |
| Tasks | Opinion Mining, Sentiment Analysis, Tokenization |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1029/ |
| https://www.aclweb.org/anthology/S16-1029 | |
| PWC | https://paperswithcode.com/paper/dsic-elirf-at-semeval-2016-task-4-message |
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ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling
| Title | ALTO: Active Learning with Topic Overviews for Speeding Label Induction and Document Labeling |
| Authors | Forough Poursabzi-Sangdeh, Jordan Boyd-Graber, Leah Findlater, Kevin Seppi |
| Abstract | |
| Tasks | Active Learning, Text Classification, Topic Models |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1110/ |
| https://www.aclweb.org/anthology/P16-1110 | |
| PWC | https://paperswithcode.com/paper/alto-active-learning-with-topic-overviews-for |
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An Empirical Evaluation of Noise Contrastive Estimation for the Neural Network Joint Model of Translation
| Title | An Empirical Evaluation of Noise Contrastive Estimation for the Neural Network Joint Model of Translation |
| Authors | Colin Cherry |
| Abstract | |
| Tasks | Domain Adaptation, Language Modelling, Machine Translation |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1006/ |
| https://www.aclweb.org/anthology/N16-1006 | |
| PWC | https://paperswithcode.com/paper/an-empirical-evaluation-of-noise-contrastive |
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Generating Video Description using Sequence-to-sequence Model with Temporal Attention
| Title | Generating Video Description using Sequence-to-sequence Model with Temporal Attention |
| Authors | Natsuda Laokulrat, Sang Phan, Noriki Nishida, Raphael Shu, Yo Ehara, Naoaki Okazaki, Yusuke Miyao, Hideki Nakayama |
| Abstract | Automatic video description generation has recently been getting attention after rapid advancement in image caption generation. Automatically generating description for a video is more challenging than for an image due to its temporal dynamics of frames. Most of the work relied on Recurrent Neural Network (RNN) and recently attentional mechanisms have also been applied to make the model learn to focus on some frames of the video while generating each word in a describing sentence. In this paper, we focus on a sequence-to-sequence approach with temporal attention mechanism. We analyze and compare the results from different attention model configuration. By applying the temporal attention mechanism to the system, we can achieve a METEOR score of 0.310 on Microsoft Video Description dataset, which outperformed the state-of-the-art system so far. |
| Tasks | Video Classification, Video Description |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1005/ |
| https://www.aclweb.org/anthology/C16-1005 | |
| PWC | https://paperswithcode.com/paper/generating-video-description-using-sequence |
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MarsaGram: an excursion in the forests of parsing trees
| Title | MarsaGram: an excursion in the forests of parsing trees |
| Authors | Philippe Blache, St{'e}phane Rauzy, Gr{'e}goire Montcheuil |
| Abstract | The question of how to compare languages and more generally the domain of linguistic typology, relies on the study of different linguistic properties or phenomena. Classically, such a comparison is done semi-manually, for example by extracting information from databases such as the WALS. However, it remains difficult to identify precisely regular parameters, available for different languages, that can be used as a basis towards modeling. We propose in this paper, focusing on the question of syntactic typology, a method for automatically extracting such parameters from treebanks, bringing them into a typology perspective. We present the method and the tools for inferring such information and navigating through the treebanks. The approach has been applied to 10 languages of the Universal Dependencies Treebank. We approach is evaluated by showing how automatic classification correlates with language families. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1370/ |
| https://www.aclweb.org/anthology/L16-1370 | |
| PWC | https://paperswithcode.com/paper/marsagram-an-excursion-in-the-forests-of |
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On-line Multilingual Linguistic Services
| Title | On-line Multilingual Linguistic Services |
| Authors | Eric Wehrli, Yves Scherrer, Luka Nerima |
| Abstract | In this demo, we present our free on-line multilingual linguistic services which allow to analyze sentences or to extract collocations from a corpus directly on-line, or by uploading a corpus. They are available for 8 European languages (English, French, German, Greek, Italian, Portuguese, Romanian, Spanish) and can also be accessed as web services by programs. While several open systems are available for POS-tagging and dependency parsing or terminology extraction, their integration into an application requires some computational competence. Furthermore, none of the parsers/taggers handles MWEs very satisfactorily, in particular when the two terms of the collocation are distant from each other or in reverse order. Our tools, on the other hand, are specifically designed for users with no particular computational literacy. They do not require from the user any download, installation or adaptation if used on-line, and their integration in an application, using one the scripts described below is quite easy. Furthermore, by default, the parser handles collocations and other MWEs, as well as anaphora resolution (limited to 3rd person personal pronouns). When used in the tagger mode, it can be set to display grammatical functions and collocations. |
| Tasks | Dependency Parsing |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-2019/ |
| https://www.aclweb.org/anthology/C16-2019 | |
| PWC | https://paperswithcode.com/paper/on-line-multilingual-linguistic-services |
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Word Order Sensitive Embedding Features/Conditional Random Field-based Chinese Grammatical Error Detection
| Title | Word Order Sensitive Embedding Features/Conditional Random Field-based Chinese Grammatical Error Detection |
| Authors | Wei-Chieh Chou, Chin-Kui Lin, Yuan-Fu Liao, Yih-Ru Wang |
| Abstract | This paper discusses how to adapt two new word embedding features to build a more efficient Chinese Grammatical Error Diagnosis (CGED) systems to assist Chinese foreign learners (CFLs) in improving their written essays. The major idea is to apply word order sensitive Word2Vec approaches including (1) structured skip-gram and (2) continuous window (CWindow) models, because they are more suitable for solving syntax-based problems. The proposed new features were evaluated on the Test of Chinese as a Foreign Language (TOCFL) learner database provided by NLP-TEA-3{&}CGED shared task. Experimental results showed that the new features did work better than the traditional word order insensitive Word2Vec approaches. Moreover, according to the official evaluation results, our system achieved the lowest (0.1362) false positive (FA) and the highest precision rates in all three measurements. |
| Tasks | Grammatical Error Detection, Language Modelling |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4910/ |
| https://www.aclweb.org/anthology/W16-4910 | |
| PWC | https://paperswithcode.com/paper/word-order-sensitive-embedding |
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Can Text Simplification Help Machine Translation?
| Title | Can Text Simplification Help Machine Translation? |
| Authors | Sanja {\v{S}}tajner, Maja Popovic |
| Abstract | |
| Tasks | Machine Translation, Text Simplification |
| Published | 2016-01-01 |
| URL | https://www.aclweb.org/anthology/W16-3411/ |
| https://www.aclweb.org/anthology/W16-3411 | |
| PWC | https://paperswithcode.com/paper/can-text-simplification-help-machine |
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Phrase-based Machine Translation using Multiple Preordering Candidates
| Title | Phrase-based Machine Translation using Multiple Preordering Candidates |
| Authors | Yusuke Oda, Taku Kudo, Tetsuji Nakagawa, Taro Watanabe |
| Abstract | In this paper, we propose a new decoding method for phrase-based statistical machine translation which directly uses multiple preordering candidates as a graph structure. Compared with previous phrase-based decoding methods, our method is based on a simple left-to-right dynamic programming in which no decoding-time reordering is performed. As a result, its runtime is very fast and implementing the algorithm becomes easy. Our system does not depend on specific preordering methods as long as they output multiple preordering candidates, and it is trivial to employ existing preordering methods into our system. In our experiments for translating diverse 11 languages into English, the proposed method outperforms conventional phrase-based decoder in terms of translation qualities under comparable or faster decoding time. |
| Tasks | Machine Translation |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1134/ |
| https://www.aclweb.org/anthology/C16-1134 | |
| PWC | https://paperswithcode.com/paper/phrase-based-machine-translation-using |
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Corpus Resources for Dispute Mediation Discourse
| Title | Corpus Resources for Dispute Mediation Discourse |
| Authors | Mathilde Janier, Chris Reed |
| Abstract | Dispute mediation is a growing activity in the resolution of conflicts, and more and more research emerge to enhance and better understand this (until recently) understudied practice. Corpus analyses are necessary to study discourse in this context; yet, little data is available, mainly because of its confidentiality principle. After proposing hints and avenues to acquire transcripts of mediation sessions, this paper presents the Dispute Mediation Corpus, which gathers annotated excerpts of mediation dialogues. Although developed as part of a project on argumentation, it is freely available and the text data can be used by anyone. This first-ever open corpus of mediation interactions can be of interest to scholars studying discourse, but also conflict resolution, argumentation, linguistics, communication, etc. We advocate for using and extending this resource that may be valuable to a large variety of domains of research, particularly those striving to enhance the study of the rapidly growing activity of dispute mediation. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1161/ |
| https://www.aclweb.org/anthology/L16-1161 | |
| PWC | https://paperswithcode.com/paper/corpus-resources-for-dispute-mediation |
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Chinese Grammatical Error Diagnosis Using Single Word Embedding
| Title | Chinese Grammatical Error Diagnosis Using Single Word Embedding |
| Authors | Jinnan Yang, Bo Peng, Jin Wang, Jixian Zhang, Xuejie Zhang |
| Abstract | Abstract Automatic grammatical error detection for Chinese has been a big challenge for NLP researchers. Due to the formal and strict grammar rules in Chinese, it is hard for foreign students to master Chinese. A computer-assisted learning tool which can automatically detect and correct Chinese grammatical errors is necessary for those foreign students. Some of the previous works have sought to identify Chinese grammatical errors using template- and learning-based methods. In contrast, this study introduced convolutional neural network (CNN) and long-short term memory (LSTM) for the shared task of Chinese Grammatical Error Diagnosis (CGED). Different from traditional word-based embedding, single word embedding was used as input of CNN and LSTM. The proposed single word embedding can capture both semantic and syntactic information to detect those four type grammatical error. In experimental evaluation, the recall and f1-score of our submitted results Run1 of the TOCFL testing data ranked the fourth place in all submissions in detection-level. |
| Tasks | Grammatical Error Detection, Language Modelling, Multi-Label Classification |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4920/ |
| https://www.aclweb.org/anthology/W16-4920 | |
| PWC | https://paperswithcode.com/paper/chinese-grammatical-error-diagnosis-using-2 |
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Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs
| Title | Capturing Pragmatic Knowledge in Article Usage Prediction using LSTMs |
| Authors | Jad Kabbara, Yulan Feng, Jackie Chi Kit Cheung |
| Abstract | We examine the potential of recurrent neural networks for handling pragmatic inferences involving complex contextual cues for the task of article usage prediction. We train and compare several variants of Long Short-Term Memory (LSTM) networks with an attention mechanism. Our model outperforms a previous state-of-the-art system, achieving up to 96.63{%} accuracy on the WSJ/PTB corpus. In addition, we perform a series of analyses to understand the impact of various model choices. We find that the gain in performance can be attributed to the ability of LSTMs to pick up on contextual cues, both local and further away in distance, and that the model is able to solve cases involving reasoning about coreference and synonymy. We also show how the attention mechanism contributes to the interpretability of the model{'}s effectiveness. |
| Tasks | Grammatical Error Detection, Machine Translation, Word Embeddings |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1247/ |
| https://www.aclweb.org/anthology/C16-1247 | |
| PWC | https://paperswithcode.com/paper/capturing-pragmatic-knowledge-in-article |
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Bidirectional Recurrent Convolutional Neural Network for Relation Classification
| Title | Bidirectional Recurrent Convolutional Neural Network for Relation Classification |
| Authors | Rui Cai, Xiaodong Zhang, Houfeng Wang |
| Abstract | |
| Tasks | Relation Classification, Word Embeddings |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1072/ |
| https://www.aclweb.org/anthology/P16-1072 | |
| PWC | https://paperswithcode.com/paper/bidirectional-recurrent-convolutional-neural |
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The Construction of a Chinese Collocational Knowledge Resource and Its Application for Second Language Acquisition
| Title | The Construction of a Chinese Collocational Knowledge Resource and Its Application for Second Language Acquisition |
| Authors | Renfen Hu, Jiayong Chen, Kuang-hua Chen |
| Abstract | The appropriate use of collocations is a challenge for second language acquisition. However, high quality and easily accessible Chinese collocation resources are not available for both teachers and students. This paper presents the design and construction of a large scale resource of Chinese collocational knowledge, and a web-based application (OCCA, Online Chinese Collocation Assistant) which offers free and convenient collocation search service to end users. We define and classify collocations based on practical language acquisition needs and utilize a syntax based method to extract nine types of collocations. Totally 37 extraction rules are compiled with word, POS and dependency relation features, 1,750,000 collocations are extracted from a corpus for L2 learning and complementary Wikipedia data, and OCCA is implemented based on these extracted collocations. By comparing OCCA with two traditional collocation dictionaries, we find OCCA has higher entry coverage and collocation quantity, and our method achieves quite low error rate at less than 5{%}. We also discuss how to apply collocational knowledge to grammatical error detection and demonstrate comparable performance to the best results in 2015 NLP-TEA CGED shared task. The preliminary experiment shows that the collocation knowledge is helpful in detecting all the four types of grammatical errors. |
| Tasks | Grammatical Error Detection, Language Acquisition |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1307/ |
| https://www.aclweb.org/anthology/C16-1307 | |
| PWC | https://paperswithcode.com/paper/the-construction-of-a-chinese-collocational |
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Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features
| Title | Geolocation Prediction in Twitter Using Location Indicative Words and Textual Features |
| Authors | Lianhua Chi, Kwan Hui Lim, Nebula Alam, Christopher J. Butler |
| Abstract | Knowing the location of a social media user and their posts is important for various purposes, such as the recommendation of location-based items/services, and locality detection of crisis/disasters. This paper describes our submission to the shared task {``}Geolocation Prediction in Twitter{''} of the 2nd Workshop on Noisy User-generated Text. In this shared task, we propose an algorithm to predict the location of Twitter users and tweets using a multinomial Naive Bayes classifier trained on Location Indicative Words and various textual features (such as city/country names, {#}hashtags and @mentions). We compared our approach against various baselines based on Location Indicative Words, city/country names, {#}hashtags and @mentions as individual feature sets, and experimental results show that our approach outperforms these baselines in terms of classification accuracy, mean and median error distance. | |
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| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-3930/ |
| https://www.aclweb.org/anthology/W16-3930 | |
| PWC | https://paperswithcode.com/paper/geolocation-prediction-in-twitter-using |
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