Paper Group NANR 161
Solving and Generating Chinese Character Riddles. Globally Coherent Text Generation with Neural Checklist Models. Effectiveness of Linguistic and Learner Features to Listenability Measurement Using a Decision Tree Classifier. Proceedings of the Second Workshop on Computational Approaches to Deception Detection. Robust Non-Explicit Neural Discourse …
Solving and Generating Chinese Character Riddles
| Title | Solving and Generating Chinese Character Riddles |
| Authors | Chuanqi Tan, Furu Wei, Li Dong, Weifeng Lv, Ming Zhou |
| Abstract | |
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| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1081/ |
| https://www.aclweb.org/anthology/D16-1081 | |
| PWC | https://paperswithcode.com/paper/solving-and-generating-chinese-character |
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Globally Coherent Text Generation with Neural Checklist Models
| Title | Globally Coherent Text Generation with Neural Checklist Models |
| Authors | Chlo{'e} Kiddon, Luke Zettlemoyer, Yejin Choi |
| Abstract | |
| Tasks | Language Modelling, Recipe Generation, Text Generation |
| Published | 2016-11-01 |
| URL | https://www.aclweb.org/anthology/D16-1032/ |
| https://www.aclweb.org/anthology/D16-1032 | |
| PWC | https://paperswithcode.com/paper/globally-coherent-text-generation-with-neural |
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Effectiveness of Linguistic and Learner Features to Listenability Measurement Using a Decision Tree Classifier
| Title | Effectiveness of Linguistic and Learner Features to Listenability Measurement Using a Decision Tree Classifier |
| Authors | Katsunori Kotani, Takehiko Yoshimi |
| Abstract | In learning Asian languages, learners encounter the problem of character types that are different from those in their first language, for instance, between Chinese characters and the Latin alphabet. This problem also affects listening because learners reconstruct letters from speech sounds. Hence, special attention should be paid to listening practice for learners of Asian languages. However, to our knowledge, few studies have evaluated the ease of listening comprehension (listenability) in Asian languages. Therefore, as a pilot study of listenability in Asian languages, we developed a measurement method for learners of English in order to examine the discriminability of linguistic and learner features. The results showed that the accuracy of our method outperformed a simple majority vote, which suggests that a combination of linguistic and learner features should be used to measure listenability in Asian languages as well as in English. |
| Tasks | Reading Comprehension |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-4902/ |
| https://www.aclweb.org/anthology/W16-4902 | |
| PWC | https://paperswithcode.com/paper/effectiveness-of-linguistic-and-learner |
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Proceedings of the Second Workshop on Computational Approaches to Deception Detection
| Title | Proceedings of the Second Workshop on Computational Approaches to Deception Detection |
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| Abstract | |
| Tasks | Deception Detection |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-0800/ |
| https://www.aclweb.org/anthology/W16-0800 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-second-workshop-on-10 |
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Robust Non-Explicit Neural Discourse Parser in English and Chinese
| Title | Robust Non-Explicit Neural Discourse Parser in English and Chinese |
| Authors | Attapol Rutherford, Nianwen Xue |
| Abstract | |
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| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/K16-2007/ |
| https://www.aclweb.org/anthology/K16-2007 | |
| PWC | https://paperswithcode.com/paper/robust-non-explicit-neural-discourse-parser |
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Revisiting Supertagging and Parsing: How to Use Supertags in Transition-Based Parsing
| Title | Revisiting Supertagging and Parsing: How to Use Supertags in Transition-Based Parsing |
| Authors | Wonchang Chung, Suhas Siddhesh Mhatre, Alexis Nasr, Owen Rambow, Srinivas Bangalore |
| Abstract | |
| Tasks | Word Embeddings |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-3309/ |
| https://www.aclweb.org/anthology/W16-3309 | |
| PWC | https://paperswithcode.com/paper/revisiting-supertagging-and-parsing-how-to |
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Joint Learning with Global Inference for Comment Classification in Community Question Answering
| Title | Joint Learning with Global Inference for Comment Classification in Community Question Answering |
| Authors | Shafiq Joty, Llu{'\i}s M{`a}rquez, Preslav Nakov |
| Abstract | |
| Tasks | Community Question Answering, Question Answering |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1084/ |
| https://www.aclweb.org/anthology/N16-1084 | |
| PWC | https://paperswithcode.com/paper/joint-learning-with-global-inference-for |
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The Creation and Analysis of a Website Privacy Policy Corpus
| Title | The Creation and Analysis of a Website Privacy Policy Corpus |
| Authors | Shomir Wilson, Florian Schaub, Aswarth Abhilash Dara, Frederick Liu, Sushain Cherivirala, Pedro Giovanni Leon, Mads Schaarup Andersen, Sebastian Zimmeck, Kanthashree Mysore Sathyendra, N. Cameron Russell, Thomas B. Norton, Eduard Hovy, Joel Reidenberg, Norman Sadeh |
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| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1126/ |
| https://www.aclweb.org/anthology/P16-1126 | |
| PWC | https://paperswithcode.com/paper/the-creation-and-analysis-of-a-website |
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Towards Constructing Sports News from Live Text Commentary
| Title | Towards Constructing Sports News from Live Text Commentary |
| Authors | Jianmin Zhang, Jin-ge Yao, Xiaojun Wan |
| Abstract | |
| Tasks | Document Summarization, Learning-To-Rank |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1129/ |
| https://www.aclweb.org/anthology/P16-1129 | |
| PWC | https://paperswithcode.com/paper/towards-constructing-sports-news-from-live |
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Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best–Worst Scaling
| Title | Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best–Worst Scaling |
| Authors | Svetlana Kiritchenko, Saif M. Mohammad |
| Abstract | |
| Tasks | Sentiment Analysis, Stance Detection |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1095/ |
| https://www.aclweb.org/anthology/N16-1095 | |
| PWC | https://paperswithcode.com/paper/capturing-reliable-fine-grained-sentiment-1 |
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Construction of Japanese Audio-Visual Emotion Database and Its Application in Emotion Recognition
| Title | Construction of Japanese Audio-Visual Emotion Database and Its Application in Emotion Recognition |
| Authors | Nurul Lubis, R Gomez, y, Sakriani Sakti, Keisuke Nakamura, Koichiro Yoshino, Satoshi Nakamura, Kazuhiro Nakadai |
| Abstract | Emotional aspects play a vital role in making human communication a rich and dynamic experience. As we introduce more automated system in our daily lives, it becomes increasingly important to incorporate emotion to provide as natural an interaction as possible. To achieve said incorporation, rich sets of labeled emotional data is prerequisite. However, in Japanese, existing emotion database is still limited to unimodal and bimodal corpora. Since emotion is not only expressed through speech, but also visually at the same time, it is essential to include multiple modalities in an observation. In this paper, we present the first audio-visual emotion corpora in Japanese, collected from 14 native speakers. The corpus contains 100 minutes of annotated and transcribed material. We performed preliminary emotion recognition experiments on the corpus and achieved an accuracy of 61.42{%} for five classes of emotion. |
| Tasks | Emotion Recognition |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1346/ |
| https://www.aclweb.org/anthology/L16-1346 | |
| PWC | https://paperswithcode.com/paper/construction-of-japanese-audio-visual-emotion |
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Compound Type Identification in Sanskrit: What Roles do the Corpus and Grammar Play?
| Title | Compound Type Identification in Sanskrit: What Roles do the Corpus and Grammar Play? |
| Authors | Amrith Krishna, Pavankumar Satuluri, Shubham Sharma, Apurv Kumar, Pawan Goyal |
| Abstract | We propose a classification framework for semantic type identification of compounds in Sanskrit. We broadly classify the compounds into four different classes namely, \textit{Avyay{=\i}bh{=a}va}, \textit{Tatpuruṣa}, \textit{Bahuvr{=\i}hi} and \textit{Dvandva}. Our classification is based on the traditional classification system followed by the ancient grammar treatise \textit{Adṣṭ{=a}dhy{=a}y{=\i}}, proposed by P{=a}ṇini 25 centuries back. We construct an elaborate features space for our system by combining conditional rules from the grammar \textit{Adṣṭ{=a}dhy{=a}y{=\i}}, semantic relations between the compound components from a lexical database \textit{Amarakoṣa} and linguistic structures from the data using Adaptor Grammars. Our in-depth analysis of the feature space highlight inadequacy of \textit{Adṣṭ{=a}dhy{=a}y{=\i}}, a generative grammar, in classifying the data samples. Our experimental results validate the effectiveness of using lexical databases as suggested by Amba Kulkarni and Anil Kumar, and put forward a new research direction by introducing linguistic patterns obtained from Adaptor grammars for effective identification of compound type. We utilise an ensemble based approach, specifically designed for handling skewed datasets and we {%}and Experimenting with various classification methods, we achieve an overall accuracy of 0.77 using random forest classifiers. |
| Tasks | Machine Translation, Question Answering |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-3701/ |
| https://www.aclweb.org/anthology/W16-3701 | |
| PWC | https://paperswithcode.com/paper/compound-type-identification-in-sanskrit-what |
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Optimistic Bandit Convex Optimization
| Title | Optimistic Bandit Convex Optimization |
| Authors | Scott Yang, Mehryar Mohri |
| Abstract | We introduce the general and powerful scheme of predicting information re-use in optimization algorithms. This allows us to devise a computationally efficient algorithm for bandit convex optimization with new state-of-the-art guarantees for both Lipschitz loss functions and loss functions with Lipschitz gradients. This is the first algorithm admitting both a polynomial time complexity and a regret that is polynomial in the dimension of the action space that improves upon the original regret bound for Lipschitz loss functions, achieving a regret of $\widetilde O(T^{11/16}d^{3/8})$. Our algorithm further improves upon the best existing polynomial-in-dimension bound (both computationally and in terms of regret) for loss functions with Lipschitz gradients, achieving a regret of $\widetilde O(T^{8/13} d^{5/3})$. |
| Tasks | |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6429-optimistic-bandit-convex-optimization |
| http://papers.nips.cc/paper/6429-optimistic-bandit-convex-optimization.pdf | |
| PWC | https://paperswithcode.com/paper/optimistic-bandit-convex-optimization |
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UQAM-NTL: Named entity recognition in Twitter messages
| Title | UQAM-NTL: Named entity recognition in Twitter messages |
| Authors | Ngoc Tan Le, Fatma Mallek, Fatiha Sadat |
| Abstract | This paper describes our system used in the 2nd Workshop on Noisy User-generated Text (WNUT) shared task for Named Entity Recognition (NER) in Twitter, in conjunction with Coling 2016. Our system is based on supervised machine learning by applying Conditional Random Fields (CRF) to train two classifiers for two evaluations. The first evaluation aims at predicting the 10 fine-grained types of named entities; while the second evaluation aims at predicting no type of named entities. The experimental results show that our method has significantly improved Twitter NER performance. |
| Tasks | Language Modelling, Named Entity Recognition |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-3926/ |
| https://www.aclweb.org/anthology/W16-3926 | |
| PWC | https://paperswithcode.com/paper/uqam-ntl-named-entity-recognition-in-twitter |
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The Open Framework for Developing Knowledge Base And Question Answering System
| Title | The Open Framework for Developing Knowledge Base And Question Answering System |
| Authors | Jiseong Kim, GyuHyeon Choi, Jung-Uk Kim, Eun-Kyung Kim, Key-Sun Choi |
| Abstract | Developing a question answering (QA) system is a task of implementing and integrating modules of different technologies and evaluating an integrated whole system, which inevitably goes with a collaboration among experts of different domains. For supporting a easy collaboration, this demonstration presents the open framework that aims to support developing a QA system in collaborative and intuitive ways. The demonstration also shows the QA system developed by our novel framework. |
| Tasks | Question Answering, Reading Comprehension |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-2034/ |
| https://www.aclweb.org/anthology/C16-2034 | |
| PWC | https://paperswithcode.com/paper/the-open-framework-for-developing-knowledge |
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