Paper Group NANR 168
Introducing the Weighted Trustability Evaluator for Crowdsourcing Exemplified by Speaker Likability Classification. Grounded Semantic Role Labeling. Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings. Assortment Optimization Under the Mallows model. A Translation-Based Knowledge Graph Embedding Preserving Logical Pro …
Introducing the Weighted Trustability Evaluator for Crowdsourcing Exemplified by Speaker Likability Classification
| Title | Introducing the Weighted Trustability Evaluator for Crowdsourcing Exemplified by Speaker Likability Classification |
| Authors | Simone Hantke, Erik Marchi, Bj{"o}rn Schuller |
| Abstract | Crowdsourcing is an arising collaborative approach applicable among many other applications to the area of language and speech processing. In fact, the use of crowdsourcing was already applied in the field of speech processing with promising results. However, only few studies investigated the use of crowdsourcing in computational paralinguistics. In this contribution, we propose a novel evaluator for crowdsourced-based ratings termed Weighted Trustability Evaluator (WTE) which is computed from the rater-dependent consistency over the test questions. We further investigate the reliability of crowdsourced annotations as compared to the ones obtained with traditional labelling procedures, such as constrained listening experiments in laboratories or in controlled environments. This comparison includes an in-depth analysis of obtainable classification performances. The experiments were conducted on the Speaker Likability Database (SLD) already used in the INTERSPEECH Challenge 2012, and the results lend further weight to the assumption that crowdsourcing can be applied as a reliable annotation source for computational paralinguistics given a sufficient number of raters and suited measurements of their reliability. |
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| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1342/ |
| https://www.aclweb.org/anthology/L16-1342 | |
| PWC | https://paperswithcode.com/paper/introducing-the-weighted-trustability |
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Grounded Semantic Role Labeling
| Title | Grounded Semantic Role Labeling |
| Authors | Shaohua Yang, Qiaozi Gao, Changsong Liu, Caiming Xiong, Song-Chun Zhu, Joyce Y. Chai |
| Abstract | |
| Tasks | Question Answering, Semantic Role Labeling |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1019/ |
| https://www.aclweb.org/anthology/N16-1019 | |
| PWC | https://paperswithcode.com/paper/grounded-semantic-role-labeling |
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Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings
| Title | Robust Text Classification for Sparsely Labelled Data Using Multi-level Embeddings |
| Authors | Simon Baker, Douwe Kiela, Anna Korhonen |
| Abstract | The conventional solution for handling sparsely labelled data is extensive feature engineering. This is time consuming and task and domain specific. We present a novel approach for learning embedded features that aims to alleviate this problem. Our approach jointly learns embeddings at different levels of granularity (word, sentence and document) along with the class labels. The intuition is that topic semantics represented by embeddings at multiple levels results in better classification. We evaluate this approach in unsupervised and semi-supervised settings on two sparsely labelled classification tasks, outperforming the handcrafted models and several embedding baselines. |
| Tasks | Feature Engineering, Feature Selection, Named Entity Recognition, Text Classification |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/C16-1220/ |
| https://www.aclweb.org/anthology/C16-1220 | |
| PWC | https://paperswithcode.com/paper/robust-text-classification-for-sparsely |
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Assortment Optimization Under the Mallows model
| Title | Assortment Optimization Under the Mallows model |
| Authors | Antoine Desir, Vineet Goyal, Srikanth Jagabathula, Danny Segev |
| Abstract | We consider the assortment optimization problem when customer preferences follow a mixture of Mallows distributions. The assortment optimization problem focuses on determining the revenue/profit maximizing subset of products from a large universe of products; it is an important decision that is commonly faced by retailers in determining what to offer their customers. There are two key challenges: (a) the Mallows distribution lacks a closed-form expression (and requires summing an exponential number of terms) to compute the choice probability and, hence, the expected revenue/profit per customer; and (b) finding the best subset may require an exhaustive search. Our key contributions are an efficiently computable closed-form expression for the choice probability under the Mallows model and a compact mixed integer linear program (MIP) formulation for the assortment problem. |
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| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6224-assortment-optimization-under-the-mallows-model |
| http://papers.nips.cc/paper/6224-assortment-optimization-under-the-mallows-model.pdf | |
| PWC | https://paperswithcode.com/paper/assortment-optimization-under-the-mallows |
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A Translation-Based Knowledge Graph Embedding Preserving Logical Property of Relations
| Title | A Translation-Based Knowledge Graph Embedding Preserving Logical Property of Relations |
| Authors | Hee-Geun Yoon, Hyun-Je Song, Seong-Bae Park, Se-Young Park |
| Abstract | |
| Tasks | Graph Embedding, Knowledge Graph Completion, Knowledge Graph Embedding, Knowledge Graphs, Link Prediction |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1105/ |
| https://www.aclweb.org/anthology/N16-1105 | |
| PWC | https://paperswithcode.com/paper/a-translation-based-knowledge-graph-embedding |
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Proximal Deep Structured Models
| Title | Proximal Deep Structured Models |
| Authors | Shenlong Wang, Sanja Fidler, Raquel Urtasun |
| Abstract | Many problems in real-world applications involve predicting continuous-valued random variables that are statistically related. In this paper, we propose a powerful deep structured model that is able to learn complex non-linear functions which encode the dependencies between continuous output variables. We show that inference in our model using proximal methods can be efficiently solved as a feed-foward pass of a special type of deep recurrent neural network. We demonstrate the effectiveness of our approach in the tasks of image denoising, depth refinement and optical flow estimation. |
| Tasks | Denoising, Image Denoising, Optical Flow Estimation |
| Published | 2016-12-01 |
| URL | http://papers.nips.cc/paper/6074-proximal-deep-structured-models |
| http://papers.nips.cc/paper/6074-proximal-deep-structured-models.pdf | |
| PWC | https://paperswithcode.com/paper/proximal-deep-structured-models |
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My Science Tutor—Learning Science with a Conversational Virtual Tutor
| Title | My Science Tutor—Learning Science with a Conversational Virtual Tutor |
| Authors | Sameer Pradhan, Ron Cole, Wayne Ward |
| Abstract | |
| Tasks | Speech Recognition, Spoken Language Understanding |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/P16-4021/ |
| https://www.aclweb.org/anthology/P16-4021 | |
| PWC | https://paperswithcode.com/paper/my-science-tutoralearning-science-with-a |
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Enriching Source for English-to-Urdu Machine Translation
| Title | Enriching Source for English-to-Urdu Machine Translation |
| Authors | Bushra Jawaid, Amir Kamran, Ond{\v{r}}ej Bojar |
| Abstract | This paper focuses on the generation of case markers for free word order languages that use case markers as phrasal clitics for marking the relationship between the dependent-noun and its head. The generation of such clitics becomes essential task especially when translating from fixed word order languages where syntactic relations are identified by the positions of the dependent-nouns. To address the problem of missing markers on source-side, artificial markers are added in source to improve alignments with its target counterparts. Up to 1 BLEU point increase is observed over the baseline on different test sets for English-to-Urdu. |
| Tasks | Machine Translation |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-3706/ |
| https://www.aclweb.org/anthology/W16-3706 | |
| PWC | https://paperswithcode.com/paper/enriching-source-for-english-to-urdu-machine |
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A Corpus of Literal and Idiomatic Uses of German Infinitive-Verb Compounds
| Title | A Corpus of Literal and Idiomatic Uses of German Infinitive-Verb Compounds |
| Authors | Andrea Horbach, Andrea Hensler, Sabine Krome, Jakob Prange, Werner Scholze-Stubenrecht, Diana Steffen, Stefan Thater, Christian Wellner, Manfred Pinkal |
| Abstract | We present an annotation study on a representative dataset of literal and idiomatic uses of German infinitive-verb compounds in newspaper and journal texts. Infinitive-verb compounds form a challenge for writers of German, because spelling regulations are different for literal and idiomatic uses. Through the participation of expert lexicographers we were able to obtain a high-quality corpus resource which offers itself as a testbed for automatic idiomaticity detection and coarse-grained word-sense disambiguation. We trained a classifier on the corpus which was able to distinguish literal and idiomatic uses with an accuracy of 85 {%}. |
| Tasks | Word Sense Disambiguation |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1135/ |
| https://www.aclweb.org/anthology/L16-1135 | |
| PWC | https://paperswithcode.com/paper/a-corpus-of-literal-and-idiomatic-uses-of |
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Neural Utterance Ranking Model for Conversational Dialogue Systems
| Title | Neural Utterance Ranking Model for Conversational Dialogue Systems |
| Authors | Michimasa Inaba, Kenichi Takahashi |
| Abstract | |
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| Published | 2016-09-01 |
| URL | https://www.aclweb.org/anthology/W16-3648/ |
| https://www.aclweb.org/anthology/W16-3648 | |
| PWC | https://paperswithcode.com/paper/neural-utterance-ranking-model-for |
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Towards Unsupervised and Language-independent Compound Splitting using Inflectional Morphological Transformations
| Title | Towards Unsupervised and Language-independent Compound Splitting using Inflectional Morphological Transformations |
| Authors | Patrick Ziering, Lonneke van der Plas |
| Abstract | |
| Tasks | Information Retrieval, Machine Translation |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/N16-1078/ |
| https://www.aclweb.org/anthology/N16-1078 | |
| PWC | https://paperswithcode.com/paper/towards-unsupervised-and-language-independent |
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Building A Case-based Semantic English-Chinese Parallel Treebank
| Title | Building A Case-based Semantic English-Chinese Parallel Treebank |
| Authors | Huaxing Shi, Tiejun Zhao, Keh-Yih Su |
| Abstract | We construct a case-based English-to-Chinese semantic constituent parallel Treebank for a Statistical Machine Translation (SMT) task by labelling each node of the Deep Syntactic Tree (DST) with our refined semantic cases. Since subtree span-crossing is harmful in tree-based SMT, DST is adopted to alleviate this problem. At the same time, we tailor an existing case set to represent bilingual shallow semantic relations more precisely. This Treebank is a part of a semantic corpus building project, which aims to build a semantic bilingual corpus annotated with syntactic, semantic cases and word senses. Data in our Treebank is from the news domain of Datum corpus. 4,000 sentence pairs are selected to cover various lexicons and part-of-speech (POS) n-gram patterns as much as possible. This paper presents the construction of this case Treebank. Also, we have tested the effect of adopting DST structure in alleviating subtree span-crossing. Our preliminary analysis shows that the compatibility between Chinese and English trees can be significantly increased by transforming the parse-tree into the DST. Furthermore, the human agreement rate in annotation is found to be acceptable (90{%} in English nodes, 75{%} in Chinese nodes). |
| Tasks | Machine Translation |
| Published | 2016-05-01 |
| URL | https://www.aclweb.org/anthology/L16-1466/ |
| https://www.aclweb.org/anthology/L16-1466 | |
| PWC | https://paperswithcode.com/paper/building-a-case-based-semantic-english |
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Modeling non-standard language
| Title | Modeling non-standard language |
| Authors | Alex Rosen, r |
| Abstract | A specific language as used by different speakers and in different situations has a number of more or less distant varieties. Extending the notion of non-standard language to varieties that do not fit an explicitly or implicitly assumed norm or pattern, we look for methods and tools that could be applied to this domain. The needs start from the theoretical side: categories usable for the analysis of non-standard language are not readily available, and continue to methods and tools required for its detection and diagnostics. A general discussion of issues related to non-standard language is followed by two case studies. The first study presents a taxonomy of morphosyntactic categories as an attempt to analyse non-standard forms produced by non-native learners of Czech. The second study focusses on the role of a rule-based grammar and lexicon in the process of building and using a parsebank. |
| Tasks | Domain Adaptation |
| Published | 2016-12-01 |
| URL | https://www.aclweb.org/anthology/W16-3815/ |
| https://www.aclweb.org/anthology/W16-3815 | |
| PWC | https://paperswithcode.com/paper/modeling-non-standard-language |
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Driving inversion transduction grammar induction with semantic evaluation
| Title | Driving inversion transduction grammar induction with semantic evaluation |
| Authors | Meriem Beloucif, Dekai Wu |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2016-08-01 |
| URL | https://www.aclweb.org/anthology/S16-2006/ |
| https://www.aclweb.org/anthology/S16-2006 | |
| PWC | https://paperswithcode.com/paper/driving-inversion-transduction-grammar |
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Supervised Metaphor Detection using Conditional Random Fields
| Title | Supervised Metaphor Detection using Conditional Random Fields |
| Authors | Sunny Rai, Shampa Chakraverty, Devendra K. Tayal |
| Abstract | |
| Tasks | Word Embeddings |
| Published | 2016-06-01 |
| URL | https://www.aclweb.org/anthology/W16-1103/ |
| https://www.aclweb.org/anthology/W16-1103 | |
| PWC | https://paperswithcode.com/paper/supervised-metaphor-detection-using |
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