Paper Group NANR 194
CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks. SCTB: A Chinese Treebank in Scientific Domain. The QT21/HimL Combined Machine Translation System. Abu-MaTran at WMT 2016 Translation Task: Deep Learning, Morphological Segmentation and Tuning on Character Sequences. CUNI-LMU Submissions in WMT2016: Chimera Constr …
CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks
| Title | CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks |
| Authors | William Foland, James H. Martin |
| Abstract | |
| Tasks | Amr Parsing, Named Entity Recognition, Part-Of-Speech Tagging, Word Embeddings |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/papers/S16-1185/s16-1185 |
| https://www.aclweb.org/anthology/S16-1185 | |
| PWC | https://paperswithcode.com/paper/cu-nlp-at-semeval-2016-task-8-amr-parsing |
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SCTB: A Chinese Treebank in Scientific Domain
| Title | SCTB: A Chinese Treebank in Scientific Domain |
| Authors | Chenhui Chu, Toshiaki Nakazawa, Daisuke Kawahara, Sadao Kurohashi |
| Abstract | Treebanks are curial for natural language processing (NLP). In this paper, we present our work for annotating a Chinese treebank in scientific domain (SCTB), to address the problem of the lack of Chinese treebanks in this domain. Chinese analysis and machine translation experiments conducted using this treebank indicate that the annotated treebank can significantly improve the performance on both tasks. This treebank is released to promote Chinese NLP research in scientific domain. |
| Tasks | Chinese Word Segmentation, Machine Translation |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-5407/ |
| https://www.aclweb.org/anthology/W16-5407 | |
| PWC | https://paperswithcode.com/paper/sctb-a-chinese-treebank-in-scientific-domain |
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The QT21/HimL Combined Machine Translation System
| Title | The QT21/HimL Combined Machine Translation System |
| Authors | Jan-Thorsten Peter, Tamer Alkhouli, Hermann Ney, Matthias Huck, Fabienne Braune, Alex Fraser, er, Ale{\v{s}} Tamchyna, Ond{\v{r}}ej Bojar, Barry Haddow, Rico Sennrich, Fr{'e}d{'e}ric Blain, Lucia Specia, Jan Niehues, Alex Waibel, Alex Allauzen, re, Lauriane Aufrant, Franck Burlot, Elena Knyazeva, Thomas Lavergne, Fran{\c{c}}ois Yvon, M{=a}rcis Pinnis, Stella Frank |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2320/ |
| https://www.aclweb.org/anthology/W16-2320 | |
| PWC | https://paperswithcode.com/paper/the-qt21himl-combined-machine-translation |
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Abu-MaTran at WMT 2016 Translation Task: Deep Learning, Morphological Segmentation and Tuning on Character Sequences
| Title | Abu-MaTran at WMT 2016 Translation Task: Deep Learning, Morphological Segmentation and Tuning on Character Sequences |
| Authors | V{'\i}ctor M. S{'a}nchez-Cartagena, Antonio Toral |
| Abstract | |
| Tasks | Language Modelling, Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2322/ |
| https://www.aclweb.org/anthology/W16-2322 | |
| PWC | https://paperswithcode.com/paper/abu-matran-at-wmt-2016-translation-task-deep |
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CUNI-LMU Submissions in WMT2016: Chimera Constrained and Beaten
| Title | CUNI-LMU Submissions in WMT2016: Chimera Constrained and Beaten |
| Authors | Ale{\v{s}} Tamchyna, Roman Sudarikov, Ond{\v{r}}ej Bojar, Alex Fraser, er |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2325/ |
| https://www.aclweb.org/anthology/W16-2325 | |
| PWC | https://paperswithcode.com/paper/cuni-lmu-submissions-in-wmt2016-chimera |
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DFKI’s system for WMT16 IT-domain task, including analysis of systematic errors
| Title | DFKI’s system for WMT16 IT-domain task, including analysis of systematic errors |
| Authors | Eleftherios Avramidis, Aljoscha Burchardt, Vivien Macketanz, Ankit Srivastava |
| Abstract | |
| Tasks | Language Modelling, Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2329/ |
| https://www.aclweb.org/anthology/W16-2329 | |
| PWC | https://paperswithcode.com/paper/dfkis-system-for-wmt16-it-domain-task |
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A Non-parametric Learning Method for Confidently Estimating Patient’s Clinical State and Dynamics
| Title | A Non-parametric Learning Method for Confidently Estimating Patient’s Clinical State and Dynamics |
| Authors | William Hoiles, Mihaela Van Der Schaar |
| Abstract | Estimating patient’s clinical state from multiple concurrent physiological streams plays an important role in determining if a therapeutic intervention is necessary and for triaging patients in the hospital. In this paper we construct a non-parametric learning algorithm to estimate the clinical state of a patient. The algorithm addresses several known challenges with clinical state estimation such as eliminating bias introduced by therapeutic intervention censoring, increasing the timeliness of state estimation while ensuring a sufficient accuracy, and the ability to detect anomalous clinical states. These benefits are obtained by combining the tools of non-parametric Bayesian inference, permutation testing, and generalizations of the empirical Bernstein inequality. The algorithm is validated using real-world data from a cancer ward in a large academic hospital. |
| Tasks | Bayesian Inference |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6454-a-non-parametric-learning-method-for-confidently-estimating-patients-clinical-state-and-dynamics |
| http://papers.nips.cc/paper/6454-a-non-parametric-learning-method-for-confidently-estimating-patients-clinical-state-and-dynamics.pdf | |
| PWC | https://paperswithcode.com/paper/a-non-parametric-learning-method-for |
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Semantic Relation Extraction with Semantic Patterns Experiment on Radiology Reports
| Title | Semantic Relation Extraction with Semantic Patterns Experiment on Radiology Reports |
| Authors | Mathieu Lafourcade, Lionel Ramadier |
| Abstract | This work presents a practical system for indexing terms and relations from French radiology reports, called IMAIOS. In this paper, we present how semantic relations (causes, consequences, symptoms, locations, parts…) between medical terms can be extracted. For this purpose, we handcrafted some linguistic patterns from on a subset of our radiology report corpora. As semantic patterns (de (of)) may be too general or ambiguous, semantic constraints have been added. For instance, in the sentence n{'e}oplasie du sein (neoplasm of breast) the system knowing neoplasm as a disease and breast as an anatomical location, identify the relation as being a location: neoplasm r-lieu breast. An evaluation of the effect of semantic constraints is proposed. |
| Tasks | Relation Extraction |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1725/ |
| https://www.aclweb.org/anthology/L16-1725 | |
| PWC | https://paperswithcode.com/paper/semantic-relation-extraction-with-semantic |
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A CALL System for Learning Preposition Usage
| Title | A CALL System for Learning Preposition Usage |
| Authors | John Lee, Donald Sturgeon, Mengqi Luo |
| Abstract | |
| Tasks | |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1093/ |
| https://www.aclweb.org/anthology/P16-1093 | |
| PWC | https://paperswithcode.com/paper/a-call-system-for-learning-preposition-usage |
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JU-USAAR: A Domain Adaptive MT System
| Title | JU-USAAR: A Domain Adaptive MT System |
| Authors | Koushik Pahari, Alapan Kuila, Santanu Pal, Sudip Kumar Naskar, B, Sivaji yopadhyay, Josef van Genabith |
| Abstract | |
| Tasks | Domain Adaptation, Language Modelling, Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/W16-2333/ |
| https://www.aclweb.org/anthology/W16-2333 | |
| PWC | https://paperswithcode.com/paper/ju-usaar-a-domain-adaptive-mt-system |
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Recent Advances in Development of a Lexicon-Grammar of Polish: PolNet 3.0
| Title | Recent Advances in Development of a Lexicon-Grammar of Polish: PolNet 3.0 |
| Authors | Zygmunt Vetulani, Gra{.z}yna Vetulani, Bart{\l}omiej Kochanowski |
| Abstract | The granularity of PolNet (Polish Wordnet) is the main theoretical issue discussed in the paper. We describe the latest extension of PolNet including valency information of simple verbs and noun-verb collocations using manual and machine-assisted methods. Valency is defined to include both semantic and syntactic selectional restrictions. We assume the valency structure of a verb to be an index of meaning. Consistently we consider it an attribute of a synset. Strict application of this principle results in fine granularity of the verb section of the wordnet. Considering valency as a distinctive feature of synsets was an essential step to transform the initial PolNet (first intended as a lexical ontology) into a lexicon-grammar. For the present refinement of PolNet we assume that the category of language register is a part of meaning. The totality of PolNet 2.0 synsets is being revised in order to split the PolNet 2.0 synsets that contain different register words into register-uniform sub-synsets. We completed this operation for synsets that were used as values of semantic roles. The operation augmented the number of considered synsets by 29{%}. In the paper we report an extension of the class of collocation-based verb synsets. |
| Tasks | |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1455/ |
| https://www.aclweb.org/anthology/L16-1455 | |
| PWC | https://paperswithcode.com/paper/recent-advances-in-development-of-a-lexicon |
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Bi-Objective Online Matching and Submodular Allocations
| Title | Bi-Objective Online Matching and Submodular Allocations |
| Authors | Hossein Esfandiari, Nitish Korula, Vahab Mirrokni |
| Abstract | Online allocation problems have been widely studied due to their numerous practical applications (particularly to Internet advertising), as well as considerable theoretical interest. The main challenge in such problems is making assignment decisions in the face of uncertainty about future input; effective algorithms need to predict which constraints are most likely to bind, and learn the balance between short-term gain and the value of long-term resource availability. In many important applications, the algorithm designer is faced with multiple objectives to optimize. In particular, in online advertising it is fairly common to optimize multiple metrics, such as clicks, conversions, and impressions, as well as other metrics which may be largely uncorrelated such as ‘share of voice’, and ‘buyer surplus’. While there has been considerable work on multi-objective offline optimization (when the entire input is known in advance), very little is known about the online case, particularly in the case of adversarial input. In this paper, we give the first results for bi-objective online submodular optimization, providing almost matching upper and lower bounds for allocating items to agents with two submodular value functions. We also study practically relevant special cases of this problem related to Internet advertising, and obtain improved results. All our algorithms are nearly best possible, as well as being efficient and easy to implement in practice. |
| Tasks | |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6085-bi-objective-online-matching-and-submodular-allocations |
| http://papers.nips.cc/paper/6085-bi-objective-online-matching-and-submodular-allocations.pdf | |
| PWC | https://paperswithcode.com/paper/bi-objective-online-matching-and-submodular |
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Sequence-based Structured Prediction for Semantic Parsing
| Title | Sequence-based Structured Prediction for Semantic Parsing |
| Authors | Chunyang Xiao, Marc Dymetman, Claire Gardent |
| Abstract | |
| Tasks | Machine Translation, Semantic Parsing, Structured Prediction, Text Generation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-1127/ |
| https://www.aclweb.org/anthology/P16-1127 | |
| PWC | https://paperswithcode.com/paper/sequence-based-structured-prediction-for |
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Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random
| Title | Consistent Estimation of Functions of Data Missing Non-Monotonically and Not at Random |
| Authors | Ilya Shpitser |
| Abstract | Missing records are a perennial problem in analysis of complex data of all types, when the target of inference is some function of the full data law. In simple cases, where data is missing at random or completely at random (Rubin, 1976), well-known adjustments exist that result in consistent estimators of target quantities. Assumptions underlying these estimators are generally not realistic in practical missing data problems. Unfortunately, consistent estimators in more complex cases where data is missing not at random, and where no ordering on variables induces monotonicity of missingness status are not known in general, with some notable exceptions (Robins, 1997), (Tchetgen Tchetgen et al, 2016), (Sadinle and Reiter, 2016). In this paper, we propose a general class of consistent estimators for cases where data is missing not at random, and missingness status is non-monotonic. Our estimators, which are generalized inverse probability weighting estimators, make no assumptions on the underlying full data law, but instead place independence restrictions, and certain other fairly mild assumptions, on the distribution of missingness status conditional on the data. The assumptions we place on the distribution of missingness status conditional on the data can be viewed as a version of a conditional Markov random field (MRF) corresponding to a chain graph. Assumptions embedded in our model permit identification from the observed data law, and admit a natural fitting procedure based on the pseudo likelihood approach of (Besag, 1975). We illustrate our approach with a simple simulation study, and an analysis of risk of premature birth in women in Botswana exposed to highly active anti-retroviral therapy. |
| Tasks | |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6297-consistent-estimation-of-functions-of-data-missing-non-monotonically-and-not-at-random |
| http://papers.nips.cc/paper/6297-consistent-estimation-of-functions-of-data-missing-non-monotonically-and-not-at-random.pdf | |
| PWC | https://paperswithcode.com/paper/consistent-estimation-of-functions-of-data |
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Linear Feature Encoding for Reinforcement Learning
| Title | Linear Feature Encoding for Reinforcement Learning |
| Authors | Zhao Song, Ronald E. Parr, Xuejun Liao, Lawrence Carin |
| Abstract | Feature construction is of vital importance in reinforcement learning, as the quality of a value function or policy is largely determined by the corresponding features. The recent successes of deep reinforcement learning (RL) only increase the importance of understanding feature construction. Typical deep RL approaches use a linear output layer, which means that deep RL can be interpreted as a feature construction/encoding network followed by linear value function approximation. This paper develops and evaluates a theory of linear feature encoding. We extend theoretical results on feature quality for linear value function approximation from the uncontrolled case to the controlled case. We then develop a supervised linear feature encoding method that is motivated by insights from linear value function approximation theory, as well as empirical successes from deep RL. The resulting encoder is a surprisingly effective method for linear value function approximation using raw images as inputs. |
| Tasks | |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6305-linear-feature-encoding-for-reinforcement-learning |
| http://papers.nips.cc/paper/6305-linear-feature-encoding-for-reinforcement-learning.pdf | |
| PWC | https://paperswithcode.com/paper/linear-feature-encoding-for-reinforcement |
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