Paper Group NANR 155
Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover’s Distance Regularization. Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax. Scaling Up Word Clustering. Identifying First Episodes of Psychosis in Psychiatric Patient Records using Machine Learning. Cross-media Event Extraction and Recommendation. SODA …
Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover’s Distance Regularization
| Title | Inducing Bilingual Lexica From Non-Parallel Data With Earth Mover’s Distance Regularization |
| Authors | Meng Zhang, Yang Liu, Huanbo Luan, Yiqun Liu, Maosong Sun |
| Abstract | Being able to induce word translations from non-parallel data is often a prerequisite for cross-lingual processing in resource-scarce languages and domains. Previous endeavors typically simplify this task by imposing the one-to-one translation assumption, which is too strong to hold for natural languages. We remove this constraint by introducing the Earth Mover{'}s Distance into the training of bilingual word embeddings. In this way, we take advantage of its capability to handle multiple alternative word translations in a natural form of regularization. Our approach shows significant and consistent improvements across four language pairs. We also demonstrate that our approach is particularly preferable in resource-scarce settings as it only requires a minimal seed lexicon. |
| Tasks | Word Alignment, Word Embeddings |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1300/ |
| https://www.aclweb.org/anthology/C16-1300 | |
| PWC | https://paperswithcode.com/paper/inducing-bilingual-lexica-from-non-parallel |
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Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax
| Title | Crowdsourcing Complex Language Resources: Playing to Annotate Dependency Syntax |
| Authors | Bruno Guillaume, Kar{"e}n Fort, Nicolas Lefebvre |
| Abstract | This article presents the results we obtained on a complex annotation task (that of dependency syntax) using a specifically designed Game with a Purpose, ZombiLingo. We show that with suitable mechanisms (decomposition of the task, training of the players and regular control of the annotation quality during the game), it is possible to obtain annotations whose quality is significantly higher than that obtainable with a parser, provided that enough players participate. The source code of the game and the resulting annotated corpora (for French) are freely available. |
| Tasks | Active Learning, Natural Language Inference |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1286/ |
| https://www.aclweb.org/anthology/C16-1286 | |
| PWC | https://paperswithcode.com/paper/crowdsourcing-complex-language-resources |
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Scaling Up Word Clustering
| Title | Scaling Up Word Clustering |
| Authors | Jon Dehdari, Liling Tan, Josef van Genabith |
| Abstract | |
| Tasks | Chunking, Machine Translation, Word Alignment |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-3009/ |
| https://www.aclweb.org/anthology/N16-3009 | |
| PWC | https://paperswithcode.com/paper/scaling-up-word-clustering |
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Identifying First Episodes of Psychosis in Psychiatric Patient Records using Machine Learning
| Title | Identifying First Episodes of Psychosis in Psychiatric Patient Records using Machine Learning |
| Authors | Genevieve Gorrell, Sherifat Oduola, Angus Roberts, Tom Craig, Craig Morgan, Rob Stewart |
| Abstract | |
| Tasks | Epidemiology |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2927/ |
| https://www.aclweb.org/anthology/W16-2927 | |
| PWC | https://paperswithcode.com/paper/identifying-first-episodes-of-psychosis-in |
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Cross-media Event Extraction and Recommendation
| Title | Cross-media Event Extraction and Recommendation |
| Authors | Di Lu, Clare Voss, Fangbo Tao, Xiang Ren, Rachel Guan, Rostyslav Korolov, Tongtao Zhang, Dongang Wang, Hongzhi Li, Taylor Cassidy, Heng Ji, Shih-fu Chang, Jiawei Han, William Wallace, James Hendler, Mei Si, Lance Kaplan |
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| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-3015/ |
| https://www.aclweb.org/anthology/N16-3015 | |
| PWC | https://paperswithcode.com/paper/cross-media-event-extraction-and |
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SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization
| Title | SODA:Service Oriented Domain Adaptation Architecture for Microblog Categorization |
| Authors | Himanshu Sharad Bhatt, D, S apat, ipan, Peddamuthu Balaji, Shourya Roy, Sharmistha Jat, Deepali Semwal |
| Abstract | |
| Tasks | Active Learning, Domain Adaptation, Text Categorization, Transfer Learning |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-3016/ |
| https://www.aclweb.org/anthology/N16-3016 | |
| PWC | https://paperswithcode.com/paper/sodaservice-oriented-domain-adaptation |
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A Shared Task for Spoken CALL?
| Title | A Shared Task for Spoken CALL? |
| Authors | Claudia Baur, Johanna Gerlach, Manny Rayner, Martin Russell, Helmer Strik |
| Abstract | We argue that the field of spoken CALL needs a shared task in order to facilitate comparisons between different groups and methodologies, and describe a concrete example of such a task, based on data collected from a speech-enabled online tool which has been used to help young Swiss German teens practise skills in English conversation. Items are prompt-response pairs, where the prompt is a piece of German text and the response is a recorded English audio file. The task is to label pairs as {}accept{''} or {}reject{''}, accepting responses which are grammatically and linguistically correct to match a set of hidden gold standard answers as closely as possible. Initial resources are provided so that a scratch system can be constructed with a minimal investment of effort, and in particular without necessarily using a speech recogniser. Training data for the task will be released in June 2016, and test data in January 2017. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1036/ |
| https://www.aclweb.org/anthology/L16-1036 | |
| PWC | https://paperswithcode.com/paper/a-shared-task-for-spoken-call |
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Automatic Generation and Classification of Minimal Meaningful Propositions in Educational Systems
| Title | Automatic Generation and Classification of Minimal Meaningful Propositions in Educational Systems |
| Authors | Andreea Godea, Florin Bulgarov, Rodney Nielsen |
| Abstract | Truly effective and practical educational systems will only be achievable when they have the ability to fully recognize deep relationships between a learner{'}s interpretation of a subject and the desired conceptual understanding. In this paper, we take important steps in this direction by introducing a new representation of sentences {–} Minimal Meaningful Propositions (MMPs), which will allow us to significantly improve the mapping between a learner{'}s answer and the ideal response. Using this technique, we make significant progress towards highly scalable and domain independent educational systems, that will be able to operate without human intervention. Even though this is a new task, we show very good results both for the extraction of MMPs and for classification with respect to their importance. |
| Tasks | |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1304/ |
| https://www.aclweb.org/anthology/C16-1304 | |
| PWC | https://paperswithcode.com/paper/automatic-generation-and-classification-of |
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Zara The Supergirl: An Empathetic Personality Recognition System
| Title | Zara The Supergirl: An Empathetic Personality Recognition System |
| Authors | Pascale Fung, Anik Dey, Farhad Bin Siddique, Ruixi Lin, Yang Yang, Yan Wan, Ho Yin Ricky Chan |
| Abstract | |
| Tasks | Emotion Recognition, Sentiment Analysis, Speech Recognition |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-3018/ |
| https://www.aclweb.org/anthology/N16-3018 | |
| PWC | https://paperswithcode.com/paper/zara-the-supergirl-an-empathetic-personality |
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First Story Detection using Entities and Relations
| Title | First Story Detection using Entities and Relations |
| Authors | Nikolaos Panagiotou, Cem Akkaya, Kostas Tsioutsiouliklis, Vana Kalogeraki, Dimitrios Gunopulos |
| Abstract | News portals, such as Yahoo News or Google News, collect large amounts of documents from a variety of sources on a daily basis. Only a small portion of these documents can be selected and displayed on the homepage. Thus, there is a strong preference for major, recent events. In this work, we propose a scalable and accurate First Story Detection (FSD) pipeline that identifies fresh news. In comparison to other FSD systems, our method relies on relation extraction methods exploiting entities and their relations. We evaluate our pipeline using two distinct datasets from Yahoo News and Google News. Experimental results demonstrate that our method improves over the state-of-the-art systems on both datasets with constant space and time requirements. |
| Tasks | Relation Extraction |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1305/ |
| https://www.aclweb.org/anthology/C16-1305 | |
| PWC | https://paperswithcode.com/paper/first-story-detection-using-entities-and |
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MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering?
| Title | MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering? |
| Authors | Francisco Guzm{'a}n, Preslav Nakov, Llu{'\i}s M{`a}rquez |
| Abstract | |
| Tasks | Community Question Answering, Learning-To-Rank, Machine Translation, Question Answering |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/S16-1137/ |
| https://www.aclweb.org/anthology/S16-1137 | |
| PWC | https://paperswithcode.com/paper/mte-nn-at-semeval-2016-task-3-can-machine |
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Transductive Adaptation of Black Box Predictions
| Title | Transductive Adaptation of Black Box Predictions |
| Authors | St{'e}phane Clinchant, Boris Chidlovskii, Gabriela Csurka |
| Abstract | |
| Tasks | Decision Making, Denoising, Domain Adaptation, Machine Translation, Opinion Mining, Part-Of-Speech Tagging, Text Categorization, Topic Models, Transfer Learning |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-2053/ |
| https://www.aclweb.org/anthology/P16-2053 | |
| PWC | https://paperswithcode.com/paper/transductive-adaptation-of-black-box |
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Noise reduction and targeted exploration in imitation learning for Abstract Meaning Representation parsing
| Title | Noise reduction and targeted exploration in imitation learning for Abstract Meaning Representation parsing |
| Authors | James Goodman, Andreas Vlachos, Jason Naradowsky |
| Abstract | |
| Tasks | Amr Parsing, Dependency Parsing, Imitation Learning |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1001/ |
| https://www.aclweb.org/anthology/P16-1001 | |
| PWC | https://paperswithcode.com/paper/noise-reduction-and-targeted-exploration-in |
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Unsupervised Person Slot Filling based on Graph Mining
| Title | Unsupervised Person Slot Filling based on Graph Mining |
| Authors | Dian Yu, Heng Ji |
| Abstract | |
| Tasks | Active Learning, Dependency Parsing, Knowledge Base Population, Slot Filling |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1005/ |
| https://www.aclweb.org/anthology/P16-1005 | |
| PWC | https://paperswithcode.com/paper/unsupervised-person-slot-filling-based-on |
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Joint Inference for Event Coreference Resolution
| Title | Joint Inference for Event Coreference Resolution |
| Authors | Jing Lu, Deepak Venugopal, Vibhav Gogate, Vincent Ng |
| Abstract | Event coreference resolution is a challenging problem since it relies on several components of the information extraction pipeline that typically yield noisy outputs. We hypothesize that exploiting the inter-dependencies between these components can significantly improve the performance of an event coreference resolver, and subsequently propose a novel joint inference based event coreference resolver using Markov Logic Networks (MLNs). However, the rich features that are important for this task are typically very hard to explicitly encode as MLN formulas since they significantly increase the size of the MLN, thereby making joint inference and learning infeasible. To address this problem, we propose a novel solution where we implicitly encode rich features into our model by augmenting the MLN distribution with low dimensional unit clauses. Our approach achieves state-of-the-art results on two standard evaluation corpora. |
| Tasks | Coreference Resolution |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1308/ |
| https://www.aclweb.org/anthology/C16-1308 | |
| PWC | https://paperswithcode.com/paper/joint-inference-for-event-coreference |
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