Paper Group NANR 66
Carcinologic Speech Severity Index Project: A Database of Speech Disorder Productions to Assess Quality of Life Related to Speech After Cancer. The GermaParl Corpus of Parliamentary Protocols. Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media. Exploring Named Entity Recognition As an Auxiliary Task for …
Carcinologic Speech Severity Index Project: A Database of Speech Disorder Productions to Assess Quality of Life Related to Speech After Cancer
| Title | Carcinologic Speech Severity Index Project: A Database of Speech Disorder Productions to Assess Quality of Life Related to Speech After Cancer |
| Authors | Corine Ast{'e}sano, Mathieu Balaguer, J{'e}r{^o}me Farinas, Corinne Fredouille, Pascal Gaillard, Alain Ghio, Imed Laaridh, Muriel Lalain, Beno{^\i}t Lepage, Julie Mauclair, Olivier Nocaudie, Julien Pinquier, Oriol Pont, Gilles Pouchoulin, Mich{`e}le Puech, Dani{`e}le Robert, Etienne Sicard, Virginie Woisard |
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| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1673/ |
| https://www.aclweb.org/anthology/L18-1673 | |
| PWC | https://paperswithcode.com/paper/carcinologic-speech-severity-index-project-a |
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The GermaParl Corpus of Parliamentary Protocols
| Title | The GermaParl Corpus of Parliamentary Protocols |
| Authors | Andreas Bl{"a}tte, Andre Blessing |
| Abstract | |
| Tasks | Decision Making, Machine Translation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1130/ |
| https://www.aclweb.org/anthology/L18-1130 | |
| PWC | https://paperswithcode.com/paper/the-germaparl-corpus-of-parliamentary |
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Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media
| Title | Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media |
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| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-3500/ |
| https://www.aclweb.org/anthology/W18-3500 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-sixth-international |
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Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding
| Title | Exploring Named Entity Recognition As an Auxiliary Task for Slot Filling in Conversational Language Understanding |
| Authors | Samuel Louvan, Bernardo Magnini |
| Abstract | Slot filling is a crucial task in the Natural Language Understanding (NLU) component of a dialogue system. Most approaches for this task rely solely on the domain-specific datasets for training. We propose a joint model of slot filling and Named Entity Recognition (NER) in a multi-task learning (MTL) setup. Our experiments on three slot filling datasets show that using NER as an auxiliary task improves slot filling performance and achieve competitive performance compared with state-of-the-art. In particular, NER is effective when supervised at the lower layer of the model. For low-resource scenarios, we found that MTL is effective for one dataset. |
| Tasks | Domain Adaptation, Multi-Task Learning, Named Entity Recognition, Slot Filling, Transfer Learning |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-5711/ |
| https://www.aclweb.org/anthology/W18-5711 | |
| PWC | https://paperswithcode.com/paper/exploring-named-entity-recognition-as-an |
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The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task
| Title | The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task |
| Authors | Nick Rossenbach, Jan Rosendahl, Yunsu Kim, Miguel Gra{\c{c}}a, Aman Gokrani, Hermann Ney |
| Abstract | This paper describes the submission of RWTH Aachen University for the De→En parallel corpus filtering task of the \textit{EMNLP 2018 Third Conference on Machine Translation} (WMT 2018). We use several rule-based, heuristic methods to preselect sentence pairs. These sentence pairs are scored with count-based and neural systems as language and translation models. In addition to single sentence-pair scoring, we further implement a simple redundancy removing heuristic. Our best performing corpus filtering system relies on recurrent neural language models and translation models based on the transformer architecture. A model trained on 10M randomly sampled tokens reaches a performance of 9.2{%} BLEU on newstest2018. Using our filtering and ranking techniques we achieve 34.8{%} BLEU. |
| Tasks | Machine Translation, Tokenization |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-6487/ |
| https://www.aclweb.org/anthology/W18-6487 | |
| PWC | https://paperswithcode.com/paper/the-rwth-aachen-university-filtering-system |
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Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition
| Title | Fine-grained Coordinated Cross-lingual Text Stream Alignment for Endless Language Knowledge Acquisition |
| Authors | Tao Ge, Qing Dou, Heng Ji, Lei Cui, Baobao Chang, Zhifang Sui, Furu Wei, Ming Zhou |
| Abstract | This paper proposes to study fine-grained coordinated cross-lingual text stream alignment through a novel information network decipherment paradigm. We use Burst Information Networks as media to represent text streams and present a simple yet effective network decipherment algorithm with diverse clues to decipher the networks for accurate text stream alignment. Experiments on Chinese-English news streams show our approach not only outperforms previous approaches on bilingual lexicon extraction from coordinated text streams but also can harvest high-quality alignments from large amounts of streaming data for endless language knowledge mining, which makes it promising to be a new paradigm for automatic language knowledge acquisition. |
| Tasks | Information Retrieval |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1271/ |
| https://www.aclweb.org/anthology/D18-1271 | |
| PWC | https://paperswithcode.com/paper/fine-grained-coordinated-cross-lingual-text |
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Completing State Representations using Spectral Learning
| Title | Completing State Representations using Spectral Learning |
| Authors | Nan Jiang, Alex Kulesza, Satinder Singh |
| Abstract | A central problem in dynamical system modeling is state discovery—that is, finding a compact summary of the past that captures the information needed to predict the future. Predictive State Representations (PSRs) enable clever spectral methods for state discovery; however, while consistent in the limit of infinite data, these methods often suffer from poor performance in the low data regime. In this paper we develop a novel algorithm for incorporating domain knowledge, in the form of an imperfect state representation, as side information to speed spectral learning for PSRs. We prove theoretical results characterizing the relevance of a user-provided state representation, and design spectral algorithms that can take advantage of a relevant representation. Our algorithm utilizes principal angles to extract the relevant components of the representation, and is robust to misspecification. Empirical evaluation on synthetic HMMs, an aircraft identification domain, and a gene splice dataset shows that, even with weak domain knowledge, the algorithm can significantly outperform standard PSR learning. |
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| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7686-completing-state-representations-using-spectral-learning |
| http://papers.nips.cc/paper/7686-completing-state-representations-using-spectral-learning.pdf | |
| PWC | https://paperswithcode.com/paper/completing-state-representations-using |
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Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing
| Title | Proceedings of the Workshop on Linguistic Complexity and Natural Language Processing |
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| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4600/ |
| https://www.aclweb.org/anthology/W18-4600 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-workshop-on-linguistic |
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Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations
| Title | Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations |
| Authors | Erik-Lân Do Dinh, Hannah Wieland, Iryna Gurevych |
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| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/papers/D18-1171/d18-1171 |
| https://www.aclweb.org/anthology/D18-1171 | |
| PWC | https://paperswithcode.com/paper/weeding-out-conventionalized-metaphors-a |
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DialEdit: Annotations for Spoken Conversational Image Editing
| Title | DialEdit: Annotations for Spoken Conversational Image Editing |
| Authors | Ramesh Manuvirakurike, Jacqueline Brixey, Trung Bui, Walter Chang, Ron Artstein, Kallirroi Georgila |
| Abstract | |
| Tasks | Dialogue State Tracking |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4701/ |
| https://www.aclweb.org/anthology/W18-4701 | |
| PWC | https://paperswithcode.com/paper/dialedit-annotations-for-spoken |
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Downward Compatible Revision of Dialogue Annotation
| Title | Downward Compatible Revision of Dialogue Annotation |
| Authors | Harry Bunt, Emer Gilmartin, Simon Keizer, Catherine Pelachaud, Volha Petukhova, Laurent Pr{'e}vot, Mari{"e}t Theune |
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| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4703/ |
| https://www.aclweb.org/anthology/W18-4703 | |
| PWC | https://paperswithcode.com/paper/downward-compatible-revision-of-dialogue |
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Contextual Dependencies in Time-Continuous Multidimensional Affect Recognition
| Title | Contextual Dependencies in Time-Continuous Multidimensional Affect Recognition |
| Authors | Dmitrii Fedotov, Denis Ivanko, Maxim Sidorov, Wolfgang Minker |
| Abstract | |
| Tasks | Emotion Recognition, Multimodal Emotion Recognition, Speech Recognition |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1196/ |
| https://www.aclweb.org/anthology/L18-1196 | |
| PWC | https://paperswithcode.com/paper/contextual-dependencies-in-time-continuous |
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Universal Morphologies for the Caucasus region
| Title | Universal Morphologies for the Caucasus region |
| Authors | Christian Chiarcos, Don, Kathrin t, Maxim Ionov, Monika Rind-Pawlowski, Hasmik Sargsian, Jesse Wichers Schreur, Frank Abromeit, Christian F{"a}th |
| Abstract | |
| Tasks | Lemmatization |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1417/ |
| https://www.aclweb.org/anthology/L18-1417 | |
| PWC | https://paperswithcode.com/paper/universal-morphologies-for-the-caucasus |
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Discourse Annotation in the PDTB: The Next Generation
| Title | Discourse Annotation in the PDTB: The Next Generation |
| Authors | Rashmi Prasad, Bonnie Webber, Alan Lee |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4710/ |
| https://www.aclweb.org/anthology/W18-4710 | |
| PWC | https://paperswithcode.com/paper/discourse-annotation-in-the-pdtb-the-next |
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Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization
| Title | Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization |
| Authors | Yuanxiang Gao, Li Chen, Baochun Li |
| Abstract | Training deep neural networks requires an exorbitant amount of computation resources, including a heterogeneous mix of GPU and CPU devices. It is critical to place operations in a neural network on these devices in an optimal way, so that the training process can complete within the shortest amount of time. The state-of-the-art uses reinforcement learning to learn placement skills by repeatedly performing Monte-Carlo experiments. However, due to its equal treatment of placement samples, we argue that there remains ample room for significant improvements. In this paper, we propose a new joint learning algorithm, called Post, that integrates cross-entropy minimization and proximal policy optimization to achieve theoretically guaranteed optimal efficiency. In order to incorporate the cross-entropy method as a sampling technique, we propose to represent placements using discrete probability distributions, which allows us to estimate an optimal probability mass by maximal likelihood estimation, a powerful tool with the best possible efficiency. We have implemented Post in the Google Cloud platform, and our extensive experiments with several popular neural network training benchmarks have demonstrated clear evidence of superior performance: with the same amount of learning time, it leads to placements that have training times up to 63.7% shorter over the state-of-the-art. |
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| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/8202-post-device-placement-with-cross-entropy-minimization-and-proximal-policy-optimization |
| http://papers.nips.cc/paper/8202-post-device-placement-with-cross-entropy-minimization-and-proximal-policy-optimization.pdf | |
| PWC | https://paperswithcode.com/paper/post-device-placement-with-cross-entropy |
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