Paper Group NANR 165
It’s going to be okay: Measuring Access to Support in Online Communities. Hierarchical Multi-Label Classification Networks. Massively Translingual Compound Analysis and Translation Discovery. KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus. Stylistically User-Specific Generation. Annotation of the Syntax/Semantics interface as a Bri …
It’s going to be okay: Measuring Access to Support in Online Communities
| Title | It’s going to be okay: Measuring Access to Support in Online Communities |
| Authors | Zijian Wang, David Jurgens |
| Abstract | People use online platforms to seek out support for their informational and emotional needs. Here, we ask what effect does revealing one{'}s gender have on receiving support. To answer this, we create (i) a new dataset and method for identifying supportive replies and (ii) new methods for inferring gender from text and name. We apply these methods to create a new massive corpus of 102M online interactions with gender-labeled users, each rated by degree of supportiveness. Our analysis shows wide-spread and consistent disparity in support: identifying as a woman is associated with higher rates of support - but also higher rates of disparagement. |
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| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1004/ |
| https://www.aclweb.org/anthology/D18-1004 | |
| PWC | https://paperswithcode.com/paper/its-going-to-be-okay-measuring-access-to |
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Hierarchical Multi-Label Classification Networks
| Title | Hierarchical Multi-Label Classification Networks |
| Authors | Jonatas Wehrmann, Ricardo Cerri, Rodrigo Barros |
| Abstract | One of the most challenging machine learning problems is a particular case of data classification in which classes are hierarchically structured and objects can be assigned to multiple paths of the class hierarchy at the same time. This task is known as hierarchical multi-label classification (HMC), with applications in text classification, image annotation, and in bioinformatics problems such as protein function prediction. In this paper, we propose novel neural network architectures for HMC called HMCN, capable of simultaneously optimizing local and global loss functions for discovering local hierarchical class-relationships and global information from the entire class hierarchy while penalizing hierarchical violations. We evaluate its performance in 21 datasets from four distinct domains, and we compare it against the current HMC state-of-the-art approaches. Results show that HMCN substantially outperforms all baselines with statistical significance, arising as the novel state-of-the-art for HMC. |
| Tasks | Multi-Label Classification, Protein Function Prediction, Text Classification |
| Published | 2018-07-01 |
| URL | https://icml.cc/Conferences/2018/Schedule?showEvent=2306 |
| http://proceedings.mlr.press/v80/wehrmann18a/wehrmann18a.pdf | |
| PWC | https://paperswithcode.com/paper/hierarchical-multi-label-classification |
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Massively Translingual Compound Analysis and Translation Discovery
| Title | Massively Translingual Compound Analysis and Translation Discovery |
| Authors | Winston Wu, David Yarowsky |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1612/ |
| https://www.aclweb.org/anthology/L18-1612 | |
| PWC | https://paperswithcode.com/paper/massively-translingual-compound-analysis-and |
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KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus
| Title | KIT-Multi: A Translation-Oriented Multilingual Embedding Corpus |
| Authors | Thanh-Le Ha, Jan Niehues, Matthias Sperber, Ngoc Quan Pham, Alex Waibel, er |
| Abstract | |
| Tasks | Cross-Lingual Document Classification, Document Classification, Information Retrieval, Machine Translation, Multilingual Word Embeddings, Natural Language Inference, Question Answering, Semantic Textual Similarity, Transfer Learning, Word Embeddings |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1616/ |
| https://www.aclweb.org/anthology/L18-1616 | |
| PWC | https://paperswithcode.com/paper/kit-multi-a-translation-oriented-multilingual |
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Stylistically User-Specific Generation
| Title | Stylistically User-Specific Generation |
| Authors | Abdurrisyad Fikri, Hiroya Takamura, Manabu Okumura |
| Abstract | Recent neural models for response generation show good results in terms of general responses. In real conversations, however, depending on the speaker/responder, similar utterances should require different responses. In this study, we attempt to consider individual user{'}s information in adjusting the notable sequence-to-sequence (seq2seq) model for more diverse, user-specific responses. We assume that we need user-specific features to adjust the response and we argue that some selected representative words from the users are suitable for this task. Furthermore, we prove that even for unseen or unknown users, our model can provide more diverse and interesting responses, while maintaining correlation with input utterances. Experimental results with human evaluation show that our model can generate more interesting responses than the popular seq2seqmodel and achieve higher relevance with input utterances than our baseline. |
| Tasks | Machine Translation, Text Generation |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-6510/ |
| https://www.aclweb.org/anthology/W18-6510 | |
| PWC | https://paperswithcode.com/paper/stylistically-user-specific-generation |
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Annotation of the Syntax/Semantics interface as a Bridge between Deep Linguistic Parsing and TimeML
| Title | Annotation of the Syntax/Semantics interface as a Bridge between Deep Linguistic Parsing and TimeML |
| Authors | Mark-Matthias Zymla |
| Abstract | |
| Tasks | |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4706/ |
| https://www.aclweb.org/anthology/W18-4706 | |
| PWC | https://paperswithcode.com/paper/annotation-of-the-syntaxsemantics-interface |
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Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages
| Title | Proceedings of the Workshop on Computational Modeling of Polysynthetic Languages |
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| Abstract | |
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| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4800/ |
| https://www.aclweb.org/anthology/W18-4800 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-workshop-on-computational-2 |
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Proxy Clouds for Live RGB-D Stream Processing and Consolidation
| Title | Proxy Clouds for Live RGB-D Stream Processing and Consolidation |
| Authors | Adrien Kaiser, Jose Alonso Ybanez Zepeda, Tamy Boubekeur |
| Abstract | We propose a new multiplanar superstructure for unified real-time processing of RGB-D data. Modern RGB-D sensors are widely used for indoor 3D capture, with applications ranging from modeling to robotics, through augmented reality. Nevertheless, their use is limited by their low resolution, with frames often corrupted with noise, missing data and temporal inconsistencies. Our approach, named Proxy Clouds, consists in generating and updating through time a single set of compact local statistics parameterized over detected planar proxies, which are fed from raw RGB-D data. Proxy Clouds provide several processing primitives, which improve the quality of the RGB-D stream on-the-fly or lighten further operations. Experimental results confirm that our light weight analysis framework copes well with embedded execution as well as moderate memory and computational capabilities compared to state-of-the-art methods. Processing of RGB-D data with Proxy Clouds includes noise and temporal flickering removal, hole filling and resampling. As a substitute of the observed scene, our proxy cloud can additionally be applied to compression and scene reconstruction. We present experiments performed with our framework in indoor scenes of different natures within a recent open RGB-D dataset. |
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| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Adrien_Kaiser_Proxy_Clouds_for_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Adrien_Kaiser_Proxy_Clouds_for_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/proxy-clouds-for-live-rgb-d-stream-processing |
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Computational Challenges for Polysynthetic Languages
| Title | Computational Challenges for Polysynthetic Languages |
| Authors | Judith L. Klavans |
| Abstract | Given advances in computational linguistic analysis of complex languages using Machine Learning as well as standard Finite State Transducers, coupled with recent efforts in language revitalization, the time was right to organize a first workshop to bring together experts in language technology and linguists on the one hand with language practitioners and revitalization experts on the other. This one-day meeting provides a promising forum to discuss new research on polysynthetic languages in combination with the needs of linguistic communities where such languages are written and spoken. |
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| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4801/ |
| https://www.aclweb.org/anthology/W18-4801 | |
| PWC | https://paperswithcode.com/paper/computational-challenges-for-polysynthetic |
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Finite-state morphology for Kwak’wala: A phonological approach
| Title | Finite-state morphology for Kwak’wala: A phonological approach |
| Authors | Patrick Littell |
| Abstract | This paper presents the phonological layer of a Kwak{'}wala finite-state morphological transducer, using the phonological hypotheses of Lincoln and Rath (1986) and the lenient composition operation of Karttunen (1998) to mediate the complicated relationship between underlying and surface forms. The resulting system decomposes the wide variety of surface forms in such a way that the morphological layer can be specified using unique and largely concatenative morphemes. |
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| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4803/ |
| https://www.aclweb.org/anthology/W18-4803 | |
| PWC | https://paperswithcode.com/paper/finite-state-morphology-for-kwakwala-a |
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Bridge Video and Text with Cascade Syntactic Structure
| Title | Bridge Video and Text with Cascade Syntactic Structure |
| Authors | Guolong Wang, Zheng Qin, Kaiping Xu, Kai Huang, Shuxiong Ye |
| Abstract | We present a video captioning approach that encodes features by progressively completing syntactic structure (LSTM-CSS). To construct basic syntactic structure (i.e., subject, predicate, and object), we use a Conditional Random Field to label semantic representations (i.e., motions, objects). We argue that in order to improve the comprehensiveness of the description, the local features within object regions can be used to generate complementary syntactic elements (e.g., attribute, adverbial). Inspired by redundancy of human receptors, we utilize a Region Proposal Network to focus on the object regions. To model the final temporal dynamics, Recurrent Neural Network with Path Embeddings is adopted. We demonstrate the effectiveness of LSTM-CSS on generating natural sentences: 42.3{%} and 28.5{%} in terms of BLEU@4 and METEOR. Superior performance when compared to state-of-the-art methods are reported on a large video description dataset (i.e., MSR-VTT-2016). |
| Tasks | Video Captioning, Video Description |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1303/ |
| https://www.aclweb.org/anthology/C18-1303 | |
| PWC | https://paperswithcode.com/paper/bridge-video-and-text-with-cascade-syntactic |
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Automatically Tailoring Unsupervised Morphological Segmentation to the Language
| Title | Automatically Tailoring Unsupervised Morphological Segmentation to the Language |
| Authors | Esk, Ramy er, Owen Rambow, Smar Muresan, a |
| Abstract | Morphological segmentation is beneficial for several natural language processing tasks dealing with large vocabularies. Unsupervised methods for morphological segmentation are essential for handling a diverse set of languages, including low-resource languages. Eskander et al. (2016) introduced a Language Independent Morphological Segmenter (LIMS) using Adaptor Grammars (AG) based on the best-on-average performing AG configuration. However, while LIMS worked best on average and outperforms other state-of-the-art unsupervised morphological segmentation approaches, it did not provide the optimal AG configuration for five out of the six languages. We propose two language-independent classifiers that enable the selection of the optimal or nearly-optimal configuration for the morphological segmentation of unseen languages. |
| Tasks | Machine Translation, Speech Recognition |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-5808/ |
| https://www.aclweb.org/anthology/W18-5808 | |
| PWC | https://paperswithcode.com/paper/automatically-tailoring-unsupervised |
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Results of the sixth edition of the BioASQ Challenge
| Title | Results of the sixth edition of the BioASQ Challenge |
| Authors | Anastasios Nentidis, Anastasia Krithara, Konstantinos Bougiatiotis, Georgios Paliouras, Ioannis Kakadiaris |
| Abstract | This paper presents the results of the sixth edition of the BioASQ challenge. The BioASQ challenge aims at the promotion of systems and methodologies through the organization of a challenge on two tasks: semantic indexing and question answering. In total, 26 teams with more than 90 systems participated in this year{'}s challenge. As in previous years, the best systems were able to outperform the strong baselines. This suggests that state-of-the-art systems are continuously improving, pushing the frontier of research. |
| Tasks | Information Retrieval, Question Answering |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-5301/ |
| https://www.aclweb.org/anthology/W18-5301 | |
| PWC | https://paperswithcode.com/paper/results-of-the-sixth-edition-of-the-bioasq |
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DART: A Large Dataset of Dialectal Arabic Tweets
| Title | DART: A Large Dataset of Dialectal Arabic Tweets |
| Authors | Israa Alsarsour, Esraa Mohamed, Reem Suwaileh, Tamer Elsayed |
| Abstract | |
| Tasks | Information Retrieval, Speech Recognition |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1579/ |
| https://www.aclweb.org/anthology/L18-1579 | |
| PWC | https://paperswithcode.com/paper/dart-a-large-dataset-of-dialectal-arabic |
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Revisiting Distant Supervision for Relation Extraction
| Title | Revisiting Distant Supervision for Relation Extraction |
| Authors | Tingsong Jiang, Jing Liu, Chin-Yew Lin, Zhifang Sui |
| Abstract | |
| Tasks | Relation Extraction |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1566/ |
| https://www.aclweb.org/anthology/L18-1566 | |
| PWC | https://paperswithcode.com/paper/revisiting-distant-supervision-for-relation |
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