Paper Group NANR 108
On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data. The Effect of Adding Authorship Knowledge in Automated Text Scoring. A Pedagogical Application of NooJ in Language Teaching: The Adjective in Spanish and Italian. Deep Learning Architecture for Complex Word Identification. Learning Globall …
On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data
| Title | On Learning Better Embeddings from Chinese Clinical Records: Study on Combining In-Domain and Out-Domain Data |
| Authors | Yaqiang Wang, Yunhui Chen, Hongping Shu, Yongguang Jiang |
| Abstract | High quality word embeddings are of great significance to advance applications of biomedical natural language processing. In recent years, a surge of interest on how to learn good embeddings and evaluate embedding quality based on English medical text has become increasing evident, however a limited number of studies based on Chinese medical text, particularly Chinese clinical records, were performed. Herein, we proposed a novel approach of improving the quality of learned embeddings using out-domain data as a supplementary in the case of limited Chinese clinical records. Moreover, the embedding quality evaluation method was conducted based on Medical Conceptual Similarity Property. The experimental results revealed that selecting good training samples was necessary, and collecting right amount of out-domain data and trading off between the quality of embeddings and the training time consumption were essential factors for better embeddings. |
| Tasks | Disease Prediction, Information Retrieval, Language Modelling, Word Embeddings |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-2323/ |
| https://www.aclweb.org/anthology/W18-2323 | |
| PWC | https://paperswithcode.com/paper/on-learning-better-embeddings-from-chinese |
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The Effect of Adding Authorship Knowledge in Automated Text Scoring
| Title | The Effect of Adding Authorship Knowledge in Automated Text Scoring |
| Authors | Meng Zhang, Xie Chen, Ronan Cummins, {\O}istein E. Andersen, Ted Briscoe |
| Abstract | Some language exams have multiple writing tasks. When a learner writes multiple texts in a language exam, it is not surprising that the quality of these texts tends to be similar, and the existing automated text scoring (ATS) systems do not explicitly model this similarity. In this paper, we suggest that it could be useful to include the other texts written by this learner in the same exam as extra references in an ATS system. We propose various approaches of fusing information from multiple tasks and pass this authorship knowledge into our ATS model on six different datasets. We show that this can positively affect the model performance at a global level. |
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| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/W18-0536/ |
| https://www.aclweb.org/anthology/W18-0536 | |
| PWC | https://paperswithcode.com/paper/the-effect-of-adding-authorship-knowledge-in |
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A Pedagogical Application of NooJ in Language Teaching: The Adjective in Spanish and Italian
| Title | A Pedagogical Application of NooJ in Language Teaching: The Adjective in Spanish and Italian |
| Authors | Andrea Rodrigo, Mario Monteleone, Silvia Reyes |
| Abstract | In this paper, a pedagogical application of NooJ to the teaching and learning of Spanish as a foreign language is presented, which is directed to a specific addressee: learners whose mother tongue is Italian. The category {`}adjective{'} has been chosen on account of its lower frequency of occurrence in texts written in Spanish, and particularly in the Argentine Rioplatense variety, and with the aim of developing strategies to increase its use. In addition, the features that the adjective shares with other grammatical categories render it extremely productive and provide elements that enrich the learners{'} proficiency. The reference corpus contains the front pages of the Argentinian newspaper Clar{'\i}n related to an emblematic historical moment, whose starting point is 24 March 1976, when a military coup began, and covers a thirty year period until 24 March 2006. It can be seen how the term desaparecido emerges with all its cultural and social charge, providing a context which allows an approach to Rioplatense Spanish from a more comprehensive perspective. Finally, a pedagogical proposal accounting for the application of the NooJ platform in language teaching is included. | |
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| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-3807/ |
| https://www.aclweb.org/anthology/W18-3807 | |
| PWC | https://paperswithcode.com/paper/a-pedagogical-application-of-nooj-in-language |
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Deep Learning Architecture for Complex Word Identification
| Title | Deep Learning Architecture for Complex Word Identification |
| Authors | Dirk De Hertog, Ana{"\i}s Tack |
| Abstract | We describe a system for the CWI-task that includes information on 5 aspects of the (complex) lexical item, namely distributional information of the item itself, morphological structure, psychological measures, corpus-counts and topical information. We constructed a deep learning architecture that combines those features and apply it to the probabilistic and binary classification task for all English sets and Spanish. We achieved reasonable performance on all sets with best performances seen on the probabilistic task, particularly on the English news set (MAE 0.054 and F1-score of 0.872). An analysis of the results shows that reasonable performance can be achieved with a single architecture without any domain-specific tweaking of the parameter settings and that distributional features capture almost all of the information also found in hand-crafted features. |
| Tasks | Complex Word Identification, Lexical Simplification |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/W18-0539/ |
| https://www.aclweb.org/anthology/W18-0539 | |
| PWC | https://paperswithcode.com/paper/deep-learning-architecture-for-complex-word |
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Learning Globally Optimized Object Detector via Policy Gradient
| Title | Learning Globally Optimized Object Detector via Policy Gradient |
| Authors | Yongming Rao, Dahua Lin, Jiwen Lu, Jie Zhou |
| Abstract | In this paper, we propose a simple yet effective method to learn globally optimized detector for object detection, which is a simple modification to the standard cross-entropy gradient inspired by the REINFORCE algorithm. In our approach, the cross-entropy gradient is adaptively adjusted according to overall mean Average Precision (mAP) of the current state for each detection candidate, which leads to more effective gradient and global optimization of detection results, and brings no computational overhead. Benefiting from more precise gradients produced by the global optimization method, our framework significantly improves state-of-the-art object detectors. Furthermore, since our method is based on scores and bounding boxes without modification on the architecture of object detector, it can be easily applied to off-the-shelf modern object detection frameworks. |
| Tasks | Object Detection |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Rao_Learning_Globally_Optimized_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Rao_Learning_Globally_Optimized_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/learning-globally-optimized-object-detector |
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Pose Proposal Networks
| Title | Pose Proposal Networks |
| Authors | Taiki Sekii |
| Abstract | We propose a novel method to detect an unknown number of articulated 2D poses in real time. To decouple the runtime complexity of pixel-wise body part detectors from their convolutional neural network (CNN) feature map resolutions, our approach, called pose proposal networks, introduces a state-of-the-art single-shot object detection paradigm using grid-wise image feature maps in a bottom-up pose detection scenario. Body part proposals, which are represented as region proposals, and limbs are detected directly via a single-shot CNN. Specialized to such detections, a bottom-up greedy parsing step is probabilistically redesigned to take into account the global context. Experimental results on the MPII Multi-Person benchmark confirm that our method achieves 72.8% mAP comparable to state-of-the-art bottom-up approaches while its total runtime using a GeForce GTX1080Ti card reaches up to 5.6 ms (180 FPS), which exceeds the bottleneck runtimes that are observed in state-of-the-art approaches. |
| Tasks | Object Detection |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Sekii_Pose_Proposal_Networks_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Sekii_Pose_Proposal_Networks_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/pose-proposal-networks |
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Adaptive Learning with Unknown Information Flows
| Title | Adaptive Learning with Unknown Information Flows |
| Authors | Yonatan Gur, Ahmadreza Momeni |
| Abstract | An agent facing sequential decisions that are characterized by partial feedback needs to strike a balance between maximizing immediate payoffs based on available information, and acquiring new information that may be essential for maximizing future payoffs. This trade-off is captured by the multi-armed bandit (MAB) framework that has been studied and applied when at each time epoch payoff observations are collected on the actions that are selected at that epoch. In this paper we introduce a new, generalized MAB formulation in which additional information on each arm may appear arbitrarily throughout the decision horizon, and study the impact of such information flows on the achievable performance and the design of efficient decision-making policies. By obtaining matching lower and upper bounds, we characterize the (regret) complexity of this family of MAB problems as a function of the information flows. We introduce an adaptive exploration policy that, without any prior knowledge of the information arrival process, attains the best performance (in terms of regret rate) that is achievable when the information arrival process is a priori known. Our policy uses dynamically customized virtual time indexes to endogenously control the exploration rate based on the realized information arrival process. |
| Tasks | Decision Making |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7976-adaptive-learning-with-unknown-information-flows |
| http://papers.nips.cc/paper/7976-adaptive-learning-with-unknown-information-flows.pdf | |
| PWC | https://paperswithcode.com/paper/adaptive-learning-with-unknown-information |
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Predicting Second Language Learner Successes and Mistakes by Means of Conjunctive Features
| Title | Predicting Second Language Learner Successes and Mistakes by Means of Conjunctive Features |
| Authors | Yves Bestgen |
| Abstract | This paper describes the system developed by the Centre for English Corpus Linguistics for the 2018 Duolingo SLAM challenge. It aimed at predicting the successes and mistakes of second language learners on each of the words that compose the exercises they answered. Its main characteristic is to include conjunctive features, built by combining word ngrams with metadata about the user and the exercise. It achieved a relatively good performance, ranking fifth out of 15 systems. Complementary analyses carried out to gauge the contribution of the different sets of features to the performance confirmed the usefulness of the conjunctive features for the SLAM task. |
| Tasks | Language Acquisition |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/W18-0542/ |
| https://www.aclweb.org/anthology/W18-0542 | |
| PWC | https://paperswithcode.com/paper/predicting-second-language-learner-successes |
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Ensemble Romanian Dependency Parsing with Neural Networks
| Title | Ensemble Romanian Dependency Parsing with Neural Networks |
| Authors | Radu Ion, Elena Irimia, Verginica Barbu Mititelu |
| Abstract | |
| Tasks | Dependency Parsing, Word Embeddings |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1248/ |
| https://www.aclweb.org/anthology/L18-1248 | |
| PWC | https://paperswithcode.com/paper/ensemble-romanian-dependency-parsing-with |
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TMU System for SLAM-2018
| Title | TMU System for SLAM-2018 |
| Authors | Masahiro Kaneko, Tomoyuki Kajiwara, Mamoru Komachi |
| Abstract | We introduce the TMU systems for the second language acquisition modeling shared task 2018 (Settles et al., 2018). To model learner error patterns, it is necessary to maintain a considerable amount of information regarding the type of exercises learners have been learning in the past and the manner in which they answered them. Tracking an enormous learner{'}s learning history and their correct and mistaken answers is essential to predict the learner{'}s future mistakes. Therefore, we propose a model which tracks the learner{'}s learning history efficiently. Our systems ranked fourth in the English and Spanish subtasks, and fifth in the French subtask. |
| Tasks | Language Acquisition |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/W18-0544/ |
| https://www.aclweb.org/anthology/W18-0544 | |
| PWC | https://paperswithcode.com/paper/tmu-system-for-slam-2018 |
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Neural Models of Selectional Preferences for Implicit Semantic Role Labeling
| Title | Neural Models of Selectional Preferences for Implicit Semantic Role Labeling |
| Authors | Minh Le, Antske Fokkens |
| Abstract | |
| Tasks | Semantic Role Labeling |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1484/ |
| https://www.aclweb.org/anthology/L18-1484 | |
| PWC | https://paperswithcode.com/paper/neural-models-of-selectional-preferences-for |
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Char2char Generation with Reranking for the E2E NLG Challenge
| Title | Char2char Generation with Reranking for the E2E NLG Challenge |
| Authors | Shubham Agarwal, Marc Dymetman, {'E}ric Gaussier |
| Abstract | This paper describes our submission to the E2E NLG Challenge. Recently, neural seq2seq approaches have become mainstream in NLG, often resorting to pre- (respectively post-) processing \textit{delexicalization} (relexicalization) steps at the word-level to handle rare words. By contrast, we train a simple character level seq2seq model, which requires no pre/post-processing (delexicalization, tokenization or even lowercasing), with surprisingly good results. For further improvement, we explore two re-ranking approaches for scoring candidates. We also introduce a synthetic dataset creation procedure, which opens up a new way of creating artificial datasets for Natural Language Generation. |
| Tasks | Dialogue Generation, Machine Translation, Semantic Parsing, Spoken Dialogue Systems, Text Generation, Tokenization |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-6555/ |
| https://www.aclweb.org/anthology/W18-6555 | |
| PWC | https://paperswithcode.com/paper/char2char-generation-with-reranking-for-the |
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Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method
| Title | Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method |
| Authors | Yahui Liu, Wei Bi, Jun Gao, Xiaojiang Liu, Jian Yao, Shuming Shi |
| Abstract | Sequence-to-sequence neural generation models have achieved promising performance on short text conversation tasks. However, they tend to generate generic/dull responses, leading to unsatisfying dialogue experience. We observe that in the conversation tasks, each query could have multiple responses, which forms a 1-to-n or m-to-n relationship in the view of the total corpus. The objective function used in standard sequence-to-sequence models will be dominated by loss terms with generic patterns. Inspired by this observation, we introduce a statistical re-weighting method that assigns different weights for the multiple responses of the same query, and trains the common neural generation model with the weights. Experimental results on a large Chinese dialogue corpus show that our method improves the acceptance rate of generated responses compared with several baseline models and significantly reduces the number of generated generic responses. |
| Tasks | Dialogue Generation, Machine Translation, Short-Text Conversation |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1297/ |
| https://www.aclweb.org/anthology/D18-1297 | |
| PWC | https://paperswithcode.com/paper/towards-less-generic-responses-in-neural |
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From Manuscripts to Archetypes through Iterative Clustering
| Title | From Manuscripts to Archetypes through Iterative Clustering |
| Authors | Armin Hoenen |
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| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1114/ |
| https://www.aclweb.org/anthology/L18-1114 | |
| PWC | https://paperswithcode.com/paper/from-manuscripts-to-archetypes-through |
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An Automatic Learning of an Algerian Dialect Lexicon by using Multilingual Word Embeddings
| Title | An Automatic Learning of an Algerian Dialect Lexicon by using Multilingual Word Embeddings |
| Authors | Abidi Karima, Kamel Sma{"\i}li |
| Abstract | |
| Tasks | Multilingual Word Embeddings, Word Embeddings |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1133/ |
| https://www.aclweb.org/anthology/L18-1133 | |
| PWC | https://paperswithcode.com/paper/an-automatic-learning-of-an-algerian-dialect |
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