Paper Group NANR 29
Using New York Times Picks to Identify Constructive Comments. Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms. Filling the Blanks (hint: plural noun) for Mad Libs Humor. No Need to Pay Attention: Simple Recurrent Neural Networks Work!. Analyzing the Semantic Types of Claims and Premises in an Online …
Using New York Times Picks to Identify Constructive Comments
| Title | Using New York Times Picks to Identify Constructive Comments |
| Authors | Varada Kolhatkar, Maite Taboada |
| Abstract | We examine the extent to which we are able to automatically identify constructive online comments. We build several classifiers using New York Times Picks as positive examples and non-constructive thread comments from the Yahoo News Annotated Comments Corpus as negative examples of constructive online comments. We evaluate these classifiers on a crowd-annotated corpus containing 1,121 comments. Our best classifier achieves a top F1 score of 0.84. |
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| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-4218/ |
| https://www.aclweb.org/anthology/W17-4218 | |
| PWC | https://paperswithcode.com/paper/using-new-york-times-picks-to-identify |
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Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms
| Title | Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms |
| Authors | Yi-Jie Huang, Chu Hsien Su, Yi-Chun Chang, Tseng-Hsin Ting, Tzu-Yuan Fu, Rou-Min Wang, Hong-Jie Dai, Yung-Chun Chang, Jitendra Jonnagaddala, Wen-Lian Hsu |
| Abstract | The increasing popularity of social media lead users to share enormous information on the internet. This information has various application like, it can be used to develop models to understand or predict user behavior on social media platforms. For example, few online retailers have studied the shopping patterns to predict shopper{'}s pregnancy stage. Another interesting application is to use the social media platforms to analyze users{'} health-related information. In this study, we developed a tree kernel-based model to classify tweets conveying pregnancy related information using this corpus. The developed pregnancy classification model achieved an accuracy of 0.847 and an F-score of 0.565. A new corpus from popular social media platform Twitter was developed for the purpose of this study. In future, we would like to improve this corpus by reducing noise such as retweets. |
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| Published | 2017-11-01 |
| URL | https://www.aclweb.org/anthology/W17-5804/ |
| https://www.aclweb.org/anthology/W17-5804 | |
| PWC | https://paperswithcode.com/paper/incorporating-dependency-trees-improve |
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Filling the Blanks (hint: plural noun) for Mad Libs Humor
| Title | Filling the Blanks (hint: plural noun) for Mad Libs Humor |
| Authors | Nabil Hossain, John Krumm, V, Lucy erwende, Eric Horvitz, Henry Kautz |
| Abstract | Computerized generation of humor is a notoriously difficult AI problem. We develop an algorithm called Libitum that helps humans generate humor in a Mad Lib, which is a popular fill-in-the-blank game. The algorithm is based on a machine learned classifier that determines whether a potential fill-in word is funny in the context of the Mad Lib story. We use Amazon Mechanical Turk to create ground truth data and to judge humor for our classifier to mimic, and we make this data freely available. Our testing shows that Libitum successfully aids humans in filling in Mad Libs that are usually judged funnier than those filled in by humans with no computerized help. We go on to analyze why some words are better than others at making a Mad Lib funny. |
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| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/D17-1067/ |
| https://www.aclweb.org/anthology/D17-1067 | |
| PWC | https://paperswithcode.com/paper/filling-the-blanks-hint-plural-noun-for-mad |
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No Need to Pay Attention: Simple Recurrent Neural Networks Work!
| Title | No Need to Pay Attention: Simple Recurrent Neural Networks Work! |
| Authors | Ferhan Ture, Oliver Jojic |
| Abstract | First-order factoid question answering assumes that the question can be answered by a single fact in a knowledge base (KB). While this does not seem like a challenging task, many recent attempts that apply either complex linguistic reasoning or deep neural networks achieve 65{%}{–}76{%} accuracy on benchmark sets. Our approach formulates the task as two machine learning problems: detecting the entities in the question, and classifying the question as one of the relation types in the KB. We train a recurrent neural network to solve each problem. On the SimpleQuestions dataset, our approach yields substantial improvements over previously published results {—} even neural networks based on much more complex architectures. The simplicity of our approach also has practical advantages, such as efficiency and modularity, that are valuable especially in an industry setting. In fact, we present a preliminary analysis of the performance of our model on real queries from Comcast{'}s X1 entertainment platform with millions of users every day. |
| Tasks | Question Answering |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/D17-1307/ |
| https://www.aclweb.org/anthology/D17-1307 | |
| PWC | https://paperswithcode.com/paper/no-need-to-pay-attention-simple-recurrent |
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Analyzing the Semantic Types of Claims and Premises in an Online Persuasive Forum
| Title | Analyzing the Semantic Types of Claims and Premises in an Online Persuasive Forum |
| Authors | Christopher Hidey, Elena Musi, Alyssa Hwang, Smar Muresan, a, Kathy McKeown |
| Abstract | Argumentative text has been analyzed both theoretically and computationally in terms of argumentative structure that consists of argument components (e.g., claims, premises) and their argumentative relations (e.g., support, attack). Less emphasis has been placed on analyzing the semantic types of argument components. We propose a two-tiered annotation scheme to label claims and premises and their semantic types in an online persuasive forum, Change My View, with the long-term goal of understanding what makes a message persuasive. Premises are annotated with the three types of persuasive modes: ethos, logos, pathos, while claims are labeled as interpretation, evaluation, agreement, or disagreement, the latter two designed to account for the dialogical nature of our corpus. We aim to answer three questions: 1) can humans reliably annotate the semantic types of argument components? 2) are types of premises/claims positioned in recurrent orders? and 3) are certain types of claims and/or premises more likely to appear in persuasive messages than in non-persuasive messages? |
| Tasks | Argument Mining |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-5102/ |
| https://www.aclweb.org/anthology/W17-5102 | |
| PWC | https://paperswithcode.com/paper/analyzing-the-semantic-types-of-claims-and |
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Initializing neural networks for hierarchical multi-label text classification
| Title | Initializing neural networks for hierarchical multi-label text classification |
| Authors | Simon Baker, Anna Korhonen |
| Abstract | Many tasks in the biomedical domain require the assignment of one or more predefined labels to input text, where the labels are a part of a hierarchical structure (such as a taxonomy). The conventional approach is to use a one-vs.-rest (OVR) classification setup, where a binary classifier is trained for each label in the taxonomy or ontology where all instances not belonging to the class are considered negative examples. The main drawbacks to this approach are that dependencies between classes are not leveraged in the training and classification process, and the additional computational cost of training parallel classifiers. In this paper, we apply a new method for hierarchical multi-label text classification that initializes a neural network model final hidden layer such that it leverages label co-occurrence relations such as hypernymy. This approach elegantly lends itself to hierarchical classification. We evaluated this approach using two hierarchical multi-label text classification tasks in the biomedical domain using both sentence- and document-level classification. Our evaluation shows promising results for this approach. |
| Tasks | Multi-Label Classification, Multi-Label Text Classification, Text Classification |
| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/W17-2339/ |
| https://www.aclweb.org/anthology/W17-2339 | |
| PWC | https://paperswithcode.com/paper/initializing-neural-networks-for-hierarchical |
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NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance
| Title | NID-SLAM: Robust Monocular SLAM Using Normalised Information Distance |
| Authors | Geoffrey Pascoe, Will Maddern, Michael Tanner, Pedro Pinies, Paul Newman |
| Abstract | We propose a direct monocular SLAM algorithm based on the Normalised Information Distance (NID) metric. In contrast to current state-of-the-art direct methods based on photometric error minimisation, our information-theoretic NID metric provides robustness to appearance variation due to lighting, weather and structural changes in the scene. We demonstrate successful localisation and mapping across changes in lighting with a synthetic indoor scene, and across changes in weather (direct sun, rain, snow) using real-world data collected from a vehicle-mounted camera. Our approach runs in real-time on a consumer GPU using OpenGL, and provides comparable localisation accuracy to state-of-the-art photometric methods but significantly outperforms both direct and feature-based methods in robustness to appearance changes. |
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| Published | 2017-07-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2017/html/Pascoe_NID-SLAM_Robust_Monocular_CVPR_2017_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2017/papers/Pascoe_NID-SLAM_Robust_Monocular_CVPR_2017_paper.pdf | |
| PWC | https://paperswithcode.com/paper/nid-slam-robust-monocular-slam-using |
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Results of the WMT17 Metrics Shared Task
| Title | Results of the WMT17 Metrics Shared Task |
| Authors | Ond{\v{r}}ej Bojar, Yvette Graham, Amir Kamran |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-4755/ |
| https://www.aclweb.org/anthology/W17-4755 | |
| PWC | https://paperswithcode.com/paper/results-of-the-wmt17-metrics-shared-task |
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Computational Argumentation Quality Assessment in Natural Language
| Title | Computational Argumentation Quality Assessment in Natural Language |
| Authors | Henning Wachsmuth, Nona Naderi, Yufang Hou, Yonatan Bilu, Vinodkumar Prabhakaran, Tim Alberdingk Thijm, Graeme Hirst, Benno Stein |
| Abstract | Research on computational argumentation faces the problem of how to automatically assess the quality of an argument or argumentation. While different quality dimensions have been approached in natural language processing, a common understanding of argumentation quality is still missing. This paper presents the first holistic work on computational argumentation quality in natural language. We comprehensively survey the diverse existing theories and approaches to assess logical, rhetorical, and dialectical quality dimensions, and we derive a systematic taxonomy from these. In addition, we provide a corpus with 320 arguments, annotated for all 15 dimensions in the taxonomy. Our results establish a common ground for research on computational argumentation quality assessment. |
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| Published | 2017-04-01 |
| URL | https://www.aclweb.org/anthology/E17-1017/ |
| https://www.aclweb.org/anthology/E17-1017 | |
| PWC | https://paperswithcode.com/paper/computational-argumentation-quality |
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Regret Minimization in MDPs with Options without Prior Knowledge
| Title | Regret Minimization in MDPs with Options without Prior Knowledge |
| Authors | Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Emma Brunskill |
| Abstract | The option framework integrates temporal abstraction into the reinforcement learning model through the introduction of macro-actions (i.e., options). Recent works leveraged on the mapping of Markov decision processes (MDPs) with options to semi-MDPs (SMDPs) and introduced SMDP-versions of exploration-exploitation algorithms (e.g., RMAX-SMDP and UCRL-SMDP) to analyze the impact of options on the learning performance. Nonetheless, the PAC-SMDP sample complexity of RMAX-SMDP can hardly be translated into equivalent PAC-MDP theoretical guarantees, while UCRL-SMDP requires prior knowledge of the parameters characterizing the distributions of the cumulative reward and duration of each option, which are hardly available in practice. In this paper, we remove this limitation by combining the SMDP view together with the inner Markov structure of options into a novel algorithm whose regret performance matches UCRL-SMDP’s up to an additive regret term. We show scenarios where this term is negligible and the advantage of temporal abstraction is preserved. We also report preliminary empirical result supporting the theoretical findings. |
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| Published | 2017-12-01 |
| URL | http://papers.nips.cc/paper/6909-regret-minimization-in-mdps-with-options-without-prior-knowledge |
| http://papers.nips.cc/paper/6909-regret-minimization-in-mdps-with-options-without-prior-knowledge.pdf | |
| PWC | https://paperswithcode.com/paper/regret-minimization-in-mdps-with-options |
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Generating Answering Patterns from Factoid Arabic Questions
| Title | Generating Answering Patterns from Factoid Arabic Questions |
| Authors | Essia Bessaies, Slim Mesfar, Henda Ben Ghezala |
| Abstract | |
| Tasks | Information Retrieval, Question Answering, Text Generation |
| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-3803/ |
| https://www.aclweb.org/anthology/W17-3803 | |
| PWC | https://paperswithcode.com/paper/generating-answering-patterns-from-factoid |
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Citius at SemEval-2017 Task 2: Cross-Lingual Similarity from Comparable Corpora and Dependency-Based Contexts
| Title | Citius at SemEval-2017 Task 2: Cross-Lingual Similarity from Comparable Corpora and Dependency-Based Contexts |
| Authors | Pablo Gamallo |
| Abstract | This article describes the distributional strategy submitted by the Citius team to the SemEval 2017 Task 2. Even though the team participated in two subtasks, namely monolingual and cross-lingual word similarity, the article is mainly focused on the cross-lingual subtask. Our method uses comparable corpora and syntactic dependencies to extract count-based and transparent bilingual distributional contexts. The evaluation of the results show that our method is competitive with other cross-lingual strategies, even those using aligned and parallel texts. |
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| Published | 2017-08-01 |
| URL | https://www.aclweb.org/anthology/S17-2034/ |
| https://www.aclweb.org/anthology/S17-2034 | |
| PWC | https://paperswithcode.com/paper/citius-at-semeval-2017-task-2-cross-lingual |
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Remarks on Denominal -Ed Adjectives
| Title | Remarks on Denominal -Ed Adjectives |
| Authors | Tomokazu Takehisa |
| Abstract | |
| Tasks | |
| Published | 2017-11-01 |
| URL | https://www.aclweb.org/anthology/Y17-1028/ |
| https://www.aclweb.org/anthology/Y17-1028 | |
| PWC | https://paperswithcode.com/paper/remarks-on-denominal-ed-adjectives |
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Two Challenges for CI Trustworthiness and How to Address Them
| Title | Two Challenges for CI Trustworthiness and How to Address Them |
| Authors | Kevin Baum, Maximilian A. K{"o}hl, Eva Schmidt |
| Abstract | |
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| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-3701/ |
| https://www.aclweb.org/anthology/W17-3701 | |
| PWC | https://paperswithcode.com/paper/two-challenges-for-ci-trustworthiness-and-how |
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Automatic Extraction of High-Quality Example Sentences for Word Learning Using a Determinantal Point Process
| Title | Automatic Extraction of High-Quality Example Sentences for Word Learning Using a Determinantal Point Process |
| Authors | Arseny Tolmachev, Sadao Kurohashi |
| Abstract | Flashcard systems are effective tools for learning words but have their limitations in teaching word usage. To overcome this problem, we propose a novel flashcard system that shows a new example sentence on each repetition. This extension requires high-quality example sentences, automatically extracted from a huge corpus. To do this, we use a Determinantal Point Process which scales well to large data and allows to naturally represent sentence similarity and quality as features. Our human evaluation experiment on Japanese language indicates that the proposed method successfully extracted high-quality example sentences. |
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| Published | 2017-09-01 |
| URL | https://www.aclweb.org/anthology/W17-5014/ |
| https://www.aclweb.org/anthology/W17-5014 | |
| PWC | https://paperswithcode.com/paper/automatic-extraction-of-high-quality-example |
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