Paper Group NANR 132
Eyes Don’t Lie: Predicting Machine Translation Quality Using Eye Movement. 以語言模型評估學習者文句修改前後之流暢度(Using language model to assess the fluency of learners sentences edited by teachers)[In Chinese]. Investigating LSTMs for Joint Extraction of Opinion Entities and Relations. Knowledge-Driven Event Embedding for Stock Prediction. Information structure in …
Eyes Don’t Lie: Predicting Machine Translation Quality Using Eye Movement
| Title | Eyes Don’t Lie: Predicting Machine Translation Quality Using Eye Movement |
| Authors | Hassan Sajjad, Francisco Guzm{'a}n, Nadir Durrani, Ahmed Abdelali, Houda Bouamor, Irina Temnikova, Stephan Vogel |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1125/ |
| https://www.aclweb.org/anthology/N16-1125 | |
| PWC | https://paperswithcode.com/paper/eyes-dont-lie-predicting-machine-translation |
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以語言模型評估學習者文句修改前後之流暢度(Using language model to assess the fluency of learners sentences edited by teachers)[In Chinese]
| Title | 以語言模型評估學習者文句修改前後之流暢度(Using language model to assess the fluency of learners sentences edited by teachers)[In Chinese] |
| Authors | Guan-Ying Pu, Po-Lin Chen, Shih-Hung Wu |
| Abstract | |
| Tasks | Language Modelling |
| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/O16-1011/ |
| https://www.aclweb.org/anthology/O16-1011 | |
| PWC | https://paperswithcode.com/paper/aeae-aea14a-ceaa1aa34a1aousing-language-model |
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Investigating LSTMs for Joint Extraction of Opinion Entities and Relations
| Title | Investigating LSTMs for Joint Extraction of Opinion Entities and Relations |
| Authors | Arzoo Katiyar, Claire Cardie |
| Abstract | |
| Tasks | Fine-Grained Opinion Analysis, Relation Extraction |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1087/ |
| https://www.aclweb.org/anthology/P16-1087 | |
| PWC | https://paperswithcode.com/paper/investigating-lstms-for-joint-extraction-of |
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Knowledge-Driven Event Embedding for Stock Prediction
| Title | Knowledge-Driven Event Embedding for Stock Prediction |
| Authors | Xiao Ding, Yue Zhang, Ting Liu, Junwen Duan |
| Abstract | Representing structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications. Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as event-driven stock prediction. On the other hand, events extracted from raw texts do not contain background knowledge on entities and relations that they are mentioned. To address this issue, this paper proposes to leverage extra information from knowledge graph, which provides ground truth such as attributes and properties of entities and encodes valuable relations between entities. Specifically, we propose a joint model to combine knowledge graph information into the objective function of an event embedding learning model. Experiments on event similarity and stock market prediction show that our model is more capable of obtaining better event embeddings and making more accurate prediction on stock market volatilities. |
| Tasks | Information Retrieval, Open Information Extraction, Semantic Parsing, Stock Market Prediction, Stock Prediction, Tensor Networks, Word Embeddings |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1201/ |
| https://www.aclweb.org/anthology/C16-1201 | |
| PWC | https://paperswithcode.com/paper/knowledge-driven-event-embedding-for-stock |
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Information structure in the Potsdam Commentary Corpus: Topics
| Title | Information structure in the Potsdam Commentary Corpus: Topics |
| Authors | Manfred Stede, Sara Mamprin |
| Abstract | The Potsdam Commentary Corpus is a collection of 175 German newspaper commentaries annotated on a variety of different layers. This paper introduces a new layer that covers the linguistic notion of information-structural topic (not to be confused with {`}topic{'} as applied to documents in information retrieval). To our knowledge, this is the first larger topic-annotated resource for German (and one of the first for any language). We describe the annotation guidelines and the annotation process, and the results of an inter-annotator agreement study, which compare favourably to the related work. The annotated corpus is freely available for research. | |
| Tasks | Information Retrieval |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1271/ |
| https://www.aclweb.org/anthology/L16-1271 | |
| PWC | https://paperswithcode.com/paper/information-structure-in-the-potsdam |
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基於字元階層之語音合成用文脈訊息擷取(Character-Level Linguistic Features Extraction for Text-to-Speech System) [In Chinese]
| Title | 基於字元階層之語音合成用文脈訊息擷取(Character-Level Linguistic Features Extraction for Text-to-Speech System) [In Chinese] |
| Authors | Kuan-Hung Chen, Shu-Han Liao, Yuan-Fu Liao, Yih-Ru Wang |
| Abstract | |
| Tasks | |
| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/O16-1013/ |
| https://www.aclweb.org/anthology/O16-1013 | |
| PWC | https://paperswithcode.com/paper/ao14aaeaa1eae3ac-ee-acharacter-level |
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Vaidya: A Spoken Dialog System for Health Domain
| Title | Vaidya: A Spoken Dialog System for Health Domain |
| Authors | D, Prathyusha a, Brij Mohan Lal Srivastava, Manish Shrivastava |
| Abstract | |
| Tasks | Medical Diagnosis, Speech Recognition |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-6321/ |
| https://www.aclweb.org/anthology/W16-6321 | |
| PWC | https://paperswithcode.com/paper/vaidya-a-spoken-dialog-system-for-health |
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命名實體識別運用於產品同義詞擴增(Using Named Entity Recognition Increases the Synonym of Products)[In Chinese]
| Title | 命名實體識別運用於產品同義詞擴增(Using Named Entity Recognition Increases the Synonym of Products)[In Chinese] |
| Authors | Chihli Hung, Jheng-Hua Huang, Rui-Jia Zhong, Liang-Pu Chen, Ping-Che Yang |
| Abstract | |
| Tasks | Named Entity Recognition |
| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/O16-1025/ |
| https://www.aclweb.org/anthology/O16-1025 | |
| PWC | https://paperswithcode.com/paper/a12aa-eeaec-14caac34e-ausing-named-entity |
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基於深層類神經網路及表示學習技術之文件可讀性分類(Classification of Text Readability Based on Deep Neural Network and Representation Learning Techniques)[In Chinese]
| Title | 基於深層類神經網路及表示學習技術之文件可讀性分類(Classification of Text Readability Based on Deep Neural Network and Representation Learning Techniques)[In Chinese] |
| Authors | Hou-Chiang Tseng, Hsiao-Tsung Hung, Yao-Ting Sung, Berlin Chen |
| Abstract | |
| Tasks | Representation Learning |
| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/O16-1024/ |
| https://www.aclweb.org/anthology/O16-1024 | |
| PWC | https://paperswithcode.com/paper/ao14aeccc2e-ae-coa-cea1aa-eaeclassification |
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Zooming in on Gender Differences in Social Media
| Title | Zooming in on Gender Differences in Social Media |
| Authors | Aparna Garimella, Rada Mihalcea |
| Abstract | Men are from Mars and women are from Venus - or so the genre of relationship literature would have us believe. But there is some truth in this idea, and researchers in fields as diverse as psychology, sociology, and linguistics have explored ways to better understand the differences between genders. In this paper, we take another look at the problem of gender discrimination and attempt to move beyond the typical surface-level text classification approach, by (1) identifying semantic and psycholinguistic word classes that reflect systematic differences between men and women and (2) finding differences between genders in the ways they use the same words. We describe several experiments and report results on a large collection of blogs authored by men and women. |
| Tasks | Text Classification, Word Sense Disambiguation |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4301/ |
| https://www.aclweb.org/anthology/W16-4301 | |
| PWC | https://paperswithcode.com/paper/zooming-in-on-gender-differences-in-social |
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Ecological Gestures for HRI: the GEE Corpus
| Title | Ecological Gestures for HRI: the GEE Corpus |
| Authors | Maxence Girard-Rivier, Romain Magnani, V{'e}ronique Auberg{'e}, Yuko Sasa, Liliya Tsvetanova, Fr{'e}d{'e}ric Aman, Clarisse Bayol |
| Abstract | As part of a human-robot interaction project, we are interested by gestural modality as one of many ways to communicate. In order to develop a relevant gesture recognition system associated to a smart home butler robot. Our methodology is based on an IQ game-like Wizard of Oz experiment to collect spontaneous and implicitly produced gestures in an ecological context. During the experiment, the subject has to use non-verbal cues (i.e. gestures) to interact with a robot that is the referee. The subject is unaware that his gestures will be the focus of our study. In the second part of the experiment, we asked the subjects to do the gestures he had produced in the experiment, those are the explicit gestures. The implicit gestures are compared with explicitly produced ones to determine a relevant ontology. This preliminary qualitative analysis will be the base to build a big data corpus in order to optimize acceptance of the gesture dictionary in coherence with the {``}socio-affective glue{''} dynamics. | |
| Tasks | Gesture Recognition |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1235/ |
| https://www.aclweb.org/anthology/L16-1235 | |
| PWC | https://paperswithcode.com/paper/ecological-gestures-for-hri-the-gee-corpus |
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Weakly-supervised text-to-speech alignment confidence measure
| Title | Weakly-supervised text-to-speech alignment confidence measure |
| Authors | Guillaume Serri{`e}re, Christophe Cerisara, Dominique Fohr, Odile Mella |
| Abstract | This work proposes a new confidence measure for evaluating text-to-speech alignment systems outputs, which is a key component for many applications, such as semi-automatic corpus anonymization, lips syncing, film dubbing, corpus preparation for speech synthesis and speech recognition acoustic models training. This confidence measure exploits deep neural networks that are trained on large corpora without direct supervision. It is evaluated on an open-source spontaneous speech corpus and outperforms a confidence score derived from a state-of-the-art text-to-speech aligner. We further show that this confidence measure can be used to fine-tune the output of this aligner and improve the quality of the resulting alignment. |
| Tasks | Speech Recognition, Speech Synthesis |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1192/ |
| https://www.aclweb.org/anthology/C16-1192 | |
| PWC | https://paperswithcode.com/paper/weakly-supervised-text-to-speech-alignment |
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基於增強式深層類神經網路之語言辨認系統(Reinforcement Training for Deep Neural Networks-based Language Recognition)[In Chinese]
| Title | 基於增強式深層類神經網路之語言辨認系統(Reinforcement Training for Deep Neural Networks-based Language Recognition)[In Chinese] |
| Authors | Yen-Wen Hsiao, Hung-Jui Liu, Yuan-Fu Liao |
| Abstract | |
| Tasks | |
| Published | 2016-10-01 |
| URL | https://www.aclweb.org/anthology/O16-1029/ |
| https://www.aclweb.org/anthology/O16-1029 | |
| PWC | https://paperswithcode.com/paper/ao14aa14a14aeccc2e-a1eae-e34 |
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Annotating the discourse and dialogue structure of SMS message conversations
| Title | Annotating the discourse and dialogue structure of SMS message conversations |
| Authors | Nianwen Xue, Qishen Su, Sooyoung Jeong |
| Abstract | |
| Tasks | |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-1720/ |
| https://www.aclweb.org/anthology/W16-1720 | |
| PWC | https://paperswithcode.com/paper/annotating-the-discourse-and-dialogue |
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Making Dependency Labeling Simple, Fast and Accurate
| Title | Making Dependency Labeling Simple, Fast and Accurate |
| Authors | Tianxiao Shen, Tao Lei, Regina Barzilay |
| Abstract | |
| Tasks | Dependency Parsing, Representation Learning |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1126/ |
| https://www.aclweb.org/anthology/N16-1126 | |
| PWC | https://paperswithcode.com/paper/making-dependency-labeling-simple-fast-and |
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