Paper Group NANR 258
Annotating Educational Questions for Student Response Analysis. Efficient Computation of Implicational Universals in Constraint-Based Phonology Through the Hyperplane Separation Theorem. Target-Sensitive Memory Networks for Aspect Sentiment Classification. Assessing Quality Estimation Models for Sentence-Level Prediction. Spatio-temporal Transforme …
Annotating Educational Questions for Student Response Analysis
| Title | Annotating Educational Questions for Student Response Analysis |
| Authors | Andreea Godea, Rodney Nielsen |
| Abstract | |
| Tasks | Question Answering, Word Embeddings |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1562/ |
| https://www.aclweb.org/anthology/L18-1562 | |
| PWC | https://paperswithcode.com/paper/annotating-educational-questions-for-student |
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Efficient Computation of Implicational Universals in Constraint-Based Phonology Through the Hyperplane Separation Theorem
| Title | Efficient Computation of Implicational Universals in Constraint-Based Phonology Through the Hyperplane Separation Theorem |
| Authors | Giorgio Magri |
| Abstract | This paper focuses on the most basic \textit{implicational universals} in phonological theory, called \textit{T-orders} after Anttila and Andrus (2006). It develops necessary and sufficient constraint characterizations of T-orders within \textit{Harmonic Grammar} and \textit{Optimality Theory}. These conditions rest on the rich convex geometry underlying these frameworks. They are phonologically intuitive and have significant algorithmic implications. |
| Tasks | |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-5801/ |
| https://www.aclweb.org/anthology/W18-5801 | |
| PWC | https://paperswithcode.com/paper/efficient-computation-of-implicational |
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Target-Sensitive Memory Networks for Aspect Sentiment Classification
| Title | Target-Sensitive Memory Networks for Aspect Sentiment Classification |
| Authors | Shuai Wang, Sahisnu Mazumder, Bing Liu, Mianwei Zhou, Yi Chang |
| Abstract | Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. Given an aspect/target and a sentence, the task classifies the sentiment polarity expressed on the target in the sentence. Memory networks (MNs) have been used for this task recently and have achieved state-of-the-art results. In MNs, attention mechanism plays a crucial role in detecting the sentiment context for the given target. However, we found an important problem with the current MNs in performing the ASC task. Simply improving the attention mechanism will not solve it. The problem is referred to as target-sensitive sentiment, which means that the sentiment polarity of the (detected) context is dependent on the given target and it cannot be inferred from the context alone. To tackle this problem, we propose the target-sensitive memory networks (TMNs). Several alternative techniques are designed for the implementation of TMNs and their effectiveness is experimentally evaluated. |
| Tasks | Aspect-Based Sentiment Analysis, Sentiment Analysis |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1088/ |
| https://www.aclweb.org/anthology/P18-1088 | |
| PWC | https://paperswithcode.com/paper/target-sensitive-memory-networks-for-aspect |
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Assessing Quality Estimation Models for Sentence-Level Prediction
| Title | Assessing Quality Estimation Models for Sentence-Level Prediction |
| Authors | Hoang Cuong, Jia Xu |
| Abstract | This paper provides an evaluation of a wide range of advanced sentence-level Quality Estimation models, including Support Vector Regression, Ride Regression, Neural Networks, Gaussian Processes, Bayesian Neural Networks, Deep Kernel Learning and Deep Gaussian Processes. Beside the accurateness, our main concerns are also the robustness of Quality Estimation models. Our work raises the difficulty in building strong models. Specifically, we show that Quality Estimation models often behave differently in Quality Estimation feature space, depending on whether the scale of feature space is small, medium or large. We also show that Quality Estimation models often behave differently in evaluation settings, depending on whether test data come from the same domain as the training data or not. Our work suggests several strong candidates to use in different circumstances. |
| Tasks | Gaussian Processes, Machine Translation |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1129/ |
| https://www.aclweb.org/anthology/C18-1129 | |
| PWC | https://paperswithcode.com/paper/assessing-quality-estimation-models-for |
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Spatio-temporal Transformer Network for Video Restoration
| Title | Spatio-temporal Transformer Network for Video Restoration |
| Authors | Tae Hyun Kim, Mehdi S. M. Sajjadi, Michael Hirsch, Bernhard Scholkopf |
| Abstract | State-of-the-art video restoration methods integrate optical flow estimation networks to utilize temporal information. However, these networks typically consider only a pair of consecutive frames and hence are not capable of capturing long-range temporal dependencies and fall short of establishing correspondences across several timesteps. To alleviate these problems, we propose a novel Spatio-temporal Transformer Network (STTN) which handles multiple frames at once and thereby manages to mitigate the common nuisance of occlusions in optical flow estimation. Our proposed STTN comprises a module that estimates optical flow in both space and time and a resampling layer that selectively warps target frames using the estimated flow. In our experiments, we demonstrate the efficiency of the proposed network and show state-of-the-art restoration results in video super-resolution and video deblurring. |
| Tasks | Deblurring, Optical Flow Estimation, Super-Resolution, Video Super-Resolution |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Tae_Hyun_Kim_Spatio-temporal_Transformer_Network_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Tae_Hyun_Kim_Spatio-temporal_Transformer_Network_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/spatio-temporal-transformer-network-for-video |
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Improving Sign Random Projections With Additional Information
| Title | Improving Sign Random Projections With Additional Information |
| Authors | Keegan Kang, Weipin Wong |
| Abstract | Sign random projections (SRP) is a technique which allows the user to quickly estimate the angular similarity and inner products between data. We propose using additional information to improve these estimates which is easy to implement and cost efficient. We prove that the variance of our estimator is lower than the variance of SRP. Our proposed method can also be used together with other modifications of SRP, such as Super-Bit LSH (SBLSH). We demonstrate the effectiveness of our method on the MNIST test dataset and the Gisette dataset. We discuss how our proposed method can be extended to random projections or even other hashing algorithms. |
| Tasks | |
| Published | 2018-07-01 |
| URL | https://icml.cc/Conferences/2018/Schedule?showEvent=1896 |
| http://proceedings.mlr.press/v80/kang18b/kang18b.pdf | |
| PWC | https://paperswithcode.com/paper/improving-sign-random-projections-with |
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Stop Word Lists in Free Open-source Software Packages
| Title | Stop Word Lists in Free Open-source Software Packages |
| Authors | Joel Nothman, Hanmin Qin, Roman Yurchak |
| Abstract | Open-source software packages for language processing often include stop word lists. Users may apply them without awareness of their surprising omissions (e.g. {}hasn{'}t{''} but not {}hadn{'}t{''}) and inclusions ({``}computer{''}), or their incompatibility with a particular tokenizer. Motivated by issues raised about the Scikit-learn stop list, we investigate variation among and consistency within 52 popular English-language stop lists, and propose strategies for mitigating these issues. | |
| Tasks | Tokenization |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-2502/ |
| https://www.aclweb.org/anthology/W18-2502 | |
| PWC | https://paperswithcode.com/paper/stop-word-lists-in-free-open-source-software |
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Non-Projective Dependency Parsing with Non-Local Transitions
| Title | Non-Projective Dependency Parsing with Non-Local Transitions |
| Authors | Daniel Fern{'a}ndez-Gonz{'a}lez, Carlos G{'o}mez-Rodr{'\i}guez |
| Abstract | We present a novel transition system, based on the Covington non-projective parser, introducing non-local transitions that can directly create arcs involving nodes to the left of the current focus positions. This avoids the need for long sequences of No-Arcs transitions to create long-distance arcs, thus alleviating error propagation. The resulting parser outperforms the original version and achieves the best accuracy on the Stanford Dependencies conversion of the Penn Treebank among greedy transition-based parsers. |
| Tasks | Dependency Parsing |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-2109/ |
| https://www.aclweb.org/anthology/N18-2109 | |
| PWC | https://paperswithcode.com/paper/non-projective-dependency-parsing-with-non-1 |
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Graphical Nonconvex Optimization via an Adaptive Convex Relaxation
| Title | Graphical Nonconvex Optimization via an Adaptive Convex Relaxation |
| Authors | Qiang Sun, Kean Ming Tan, Han Liu, Tong Zhang |
| Abstract | We consider the problem of learning high-dimensional Gaussian graphical models. The graphical lasso is one of the most popular methods for estimating Gaussian graphical models. However, it does not achieve the oracle rate of convergence. In this paper, we propose the graphical nonconvex optimization for optimal estimation in Gaussian graphical models, which is then approximated by a sequence of convex programs. Our proposal is computationally tractable and produces an estimator that achieves the oracle rate of convergence. The statistical error introduced by the sequential approximation using a sequence of convex programs is clearly demonstrated via a contraction property. The proposed methodology is then extended to modeling semiparametric graphical models. We show via numerical studies that the proposed estimator outperforms other popular methods for estimating Gaussian graphical models. |
| Tasks | |
| Published | 2018-07-01 |
| URL | https://icml.cc/Conferences/2018/Schedule?showEvent=1955 |
| http://proceedings.mlr.press/v80/sun18c/sun18c.pdf | |
| PWC | https://paperswithcode.com/paper/graphical-nonconvex-optimization-via-an |
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Forest-Based Neural Machine Translation
| Title | Forest-Based Neural Machine Translation |
| Authors | Chunpeng Ma, Akihiro Tamura, Masao Utiyama, Tiejun Zhao, Eiichiro Sumita |
| Abstract | Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1-best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors. For statistical machine translation (SMT), forest-based methods have been proven to be effective for solving this problem, while for NMT this kind of approach has not been attempted. This paper proposes a forest-based NMT method that translates a linearized packed forest under a simple sequence-to-sequence framework (i.e., a forest-to-sequence NMT model). The BLEU score of the proposed method is higher than that of the sequence-to-sequence NMT, tree-based NMT, and forest-based SMT systems. |
| Tasks | Machine Translation |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1116/ |
| https://www.aclweb.org/anthology/P18-1116 | |
| PWC | https://paperswithcode.com/paper/forest-based-neural-machine-translation |
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SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task
| Title | SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task |
| Authors | Tao Yu, Michihiro Yasunaga, Kai Yang, Rui Zhang, Dongxu Wang, Zifan Li, Dragomir Radev |
| Abstract | Most existing studies in text-to-SQL tasks do not require generating complex SQL queries with multiple clauses or sub-queries, and generalizing to new, unseen databases. In this paper we propose SyntaxSQLNet, a syntax tree network to address the complex and cross-domain text-to-SQL generation task. SyntaxSQLNet employs a SQL specific syntax tree-based decoder with SQL generation path history and table-aware column attention encoders. We evaluate SyntaxSQLNet on a new large-scale text-to-SQL corpus containing databases with multiple tables and complex SQL queries containing multiple SQL clauses and nested queries. We use a database split setting where databases in the test set are unseen during training. Experimental results show that SyntaxSQLNet can handle a significantly greater number of complex SQL examples than prior work, outperforming the previous state-of-the-art model by 9.5{%} in exact matching accuracy. To our knowledge, we are the first to study this complex text-to-SQL task. Our task and models with the latest updates are available at \url{https://yale-lily.github.io/seq2sql/spider}. |
| Tasks | Semantic Parsing, Text-To-Sql |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1193/ |
| https://www.aclweb.org/anthology/D18-1193 | |
| PWC | https://paperswithcode.com/paper/syntaxsqlnet-syntax-tree-networks-for-complex-1 |
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Approximate message passing for amplitude based optimization
| Title | Approximate message passing for amplitude based optimization |
| Authors | Junjie Ma, Ji Xu, Arian Maleki |
| Abstract | We consider an $\ell_2$-regularized non-convex optimization problem for recovering signals from their noisy phaseless observations. We design and study the performance of a message passing algorithm that aims to solve this optimization problem. We consider the asymptotic setting $m,n \rightarrow \infty$, $m/n \rightarrow \delta$ and obtain sharp performance bounds, where $m$ is the number of measurements and $n$ is the signal dimension. We show that for complex signals the algorithm can perform accurate recovery with only $m=\left ( \frac{64}{\pi^2}-4\right)n\approx 2.5n$ measurements. Also, we provide sharp analysis on the sensitivity of the algorithm to noise. We highlight the following facts about our message passing algorithm: (i) Adding $\ell_2$ regularization to the non-convex loss function can be beneficial even in the noiseless setting; (ii) spectral initialization has marginal impact on the performance of the algorithm. |
| Tasks | |
| Published | 2018-07-01 |
| URL | https://icml.cc/Conferences/2018/Schedule?showEvent=2297 |
| http://proceedings.mlr.press/v80/ma18e/ma18e.pdf | |
| PWC | https://paperswithcode.com/paper/approximate-message-passing-for-amplitude |
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Learning Prototypical Goal Activities for Locations
| Title | Learning Prototypical Goal Activities for Locations |
| Authors | Tianyu Jiang, Ellen Riloff |
| Abstract | People go to different places to engage in activities that reflect their goals. For example, people go to restaurants to eat, libraries to study, and churches to pray. We refer to an activity that represents a common reason why people typically go to a location as a prototypical goal activity (goal-act). Our research aims to learn goal-acts for specific locations using a text corpus and semi-supervised learning. First, we extract activities and locations that co-occur in goal-oriented syntactic patterns. Next, we create an activity profile matrix and apply a semi-supervised label propagation algorithm to iteratively revise the activity strengths for different locations using a small set of labeled data. We show that this approach outperforms several baseline methods when judged against goal-acts identified by human annotators. |
| Tasks | |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1120/ |
| https://www.aclweb.org/anthology/P18-1120 | |
| PWC | https://paperswithcode.com/paper/learning-prototypical-goal-activities-for |
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Benchmarking Aggression Identification in Social Media
| Title | Benchmarking Aggression Identification in Social Media |
| Authors | Ritesh Kumar, Atul Kr. Ojha, Shervin Malmasi, Marcos Zampieri |
| Abstract | In this paper, we present the report and findings of the Shared Task on Aggression Identification organised as part of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC - 1) at COLING 2018. The task was to develop a classifier that could discriminate between Overtly Aggressive, Covertly Aggressive, and Non-aggressive texts. For this task, the participants were provided with a dataset of 15,000 aggression-annotated Facebook Posts and Comments each in Hindi (in both Roman and Devanagari script) and English for training and validation. For testing, two different sets - one from Facebook and another from a different social media - were provided. A total of 130 teams registered to participate in the task, 30 teams submitted their test runs, and finally 20 teams also sent their system description paper which are included in the TRAC workshop proceedings. The best system obtained a weighted F-score of 0.64 for both Hindi and English on the Facebook test sets, while the best scores on the surprise set were 0.60 and 0.50 for English and Hindi respectively. The results presented in this report depict how challenging the task is. The positive response from the community and the great levels of participation in the first edition of this shared task also highlights the interest in this topic. |
| Tasks | |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/W18-4401/ |
| https://www.aclweb.org/anthology/W18-4401 | |
| PWC | https://paperswithcode.com/paper/benchmarking-aggression-identification-in |
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Let’s do it ``again’': A First Computational Approach to Detecting Adverbial Presupposition Triggers
| Title | Let’s do it ``again’': A First Computational Approach to Detecting Adverbial Presupposition Triggers | |
| Authors | Andre Cianflone, Yulan Feng, Jad Kabbara, Jackie Chi Kit Cheung |
| Abstract | We introduce the novel task of predicting adverbial presupposition triggers, which is useful for natural language generation tasks such as summarization and dialogue systems. We introduce two new corpora, derived from the Penn Treebank and the Annotated English Gigaword dataset and investigate the use of a novel attention mechanism tailored to this task. Our attention mechanism augments a baseline recurrent neural network without the need for additional trainable parameters, minimizing the added computational cost of our mechanism. We demonstrate that this model statistically outperforms our baselines. |
| Tasks | Language Modelling, Text Generation |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1256/ |
| https://www.aclweb.org/anthology/P18-1256 | |
| PWC | https://paperswithcode.com/paper/letas-do-it-aagaina-a-first-computational |
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