Paper Group NANR 228
Embedding Lexical Features via Tensor Decomposition for Small Sample Humor Recognition. Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes. Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes. Using LSTMs to Assess the Obligatoriness of Phonological Distinctive Features for Phonotactic Learn …
Embedding Lexical Features via Tensor Decomposition for Small Sample Humor Recognition
| Title | Embedding Lexical Features via Tensor Decomposition for Small Sample Humor Recognition |
| Authors | Zhenjie Zhao, Andrew Cattle, Evangelos Papalexakis, Xiaojuan Ma |
| Abstract | We propose a novel tensor embedding method that can effectively extract lexical features for humor recognition. Specifically, we use word-word co-occurrence to encode the contextual content of documents, and then decompose the tensor to get corresponding vector representations. We show that this simple method can capture features of lexical humor effectively for continuous humor recognition. In particular, we achieve a distance of 0.887 on a global humor ranking task, comparable to the top performing systems from SemEval 2017 Task 6B (Potash et al., 2017) but without the need for any external training corpus. In addition, we further show that this approach is also beneficial for small sample humor recognition tasks through a semi-supervised label propagation procedure, which achieves about 0.7 accuracy on the 16000 One-Liners (Mihalcea and Strapparava, 2005) and Pun of the Day (Yang et al., 2015) humour classification datasets using only 10{%} of known labels. |
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| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1669/ |
| https://www.aclweb.org/anthology/D19-1669 | |
| PWC | https://paperswithcode.com/paper/embedding-lexical-features-via-tensor |
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Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes
| Title | Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes |
| Authors | Siqi Liu, Milos Hauskrecht |
| Abstract | Real-world event sequences consist of complex mixtures of different types of events occurring in time. An event may depend on past events of the same type, as well as, the other types. Point processes define a general class of models for event sequences. ``Regressive point processes’’ refer to point processes that directly model the dependency between an event and any past event, an example of which is a Hawkes process. In this work, we propose and develop a new nonparametric regressive point process model based on Gaussian processes. We show that our model can represent better many commonly observed real-world event sequences and capture the dependencies between events that are difficult to model using existing nonparametric Hawkes process variants. We demonstrate the improved predictive performance of our model against state-of-the-art baselines on multiple synthetic and real-world datasets. | |
| Tasks | Gaussian Processes, Point Processes |
| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/8391-nonparametric-regressive-point-processes-based-on-conditional-gaussian-processes |
| http://papers.nips.cc/paper/8391-nonparametric-regressive-point-processes-based-on-conditional-gaussian-processes.pdf | |
| PWC | https://paperswithcode.com/paper/nonparametric-regressive-point-processes |
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Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes
| Title | Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes |
| Authors | Rui Li |
| Abstract | This paper studies statistical characteristics of multivariate observations with irregular changes in their covariance structures across input space. We propose a unified nonstationary modeling framework to jointly encode the observation correlations to generate a piece-wise representation with a hyper-level Gaussian process (GP) governing the overall contour of the pieces. In particular, we couple the encoding process with automatic relevance determination (ARD) to promote sparsity to account for the inherent redundancy. The hyper GP enables us to share statistical strength among the observation variables over a collection of GPs defined within the observation pieces to characterize the variables’ respective local smoothness. Experiments conducted across domains show superior performances over the state-of-the-art methods. |
| Tasks | Gaussian Processes |
| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/8439-multivariate-sparse-coding-of-nonstationary-covariances-with-gaussian-processes |
| http://papers.nips.cc/paper/8439-multivariate-sparse-coding-of-nonstationary-covariances-with-gaussian-processes.pdf | |
| PWC | https://paperswithcode.com/paper/multivariate-sparse-coding-of-nonstationary |
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Using LSTMs to Assess the Obligatoriness of Phonological Distinctive Features for Phonotactic Learning
| Title | Using LSTMs to Assess the Obligatoriness of Phonological Distinctive Features for Phonotactic Learning |
| Authors | Nicole Mirea, Klinton Bicknell |
| Abstract | To ascertain the importance of phonetic information in the form of phonological distinctive features for the purpose of segment-level phonotactic acquisition, we compare the performance of two recurrent neural network models of phonotactic learning: one that has access to distinctive features at the start of the learning process, and one that does not. Though the predictions of both models are significantly correlated with human judgments of non-words, the feature-naive model significantly outperforms the feature-aware one in terms of probability assigned to a held-out test set of English words, suggesting that distinctive features are not obligatory for learning phonotactic patterns at the segment level. |
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| Published | 2019-07-01 |
| URL | https://www.aclweb.org/anthology/P19-1155/ |
| https://www.aclweb.org/anthology/P19-1155 | |
| PWC | https://paperswithcode.com/paper/using-lstms-to-assess-the-obligatoriness-of |
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Investigating Sub-Word Embedding Strategies for the Morphologically Rich and Free Phrase-Order Hungarian
| Title | Investigating Sub-Word Embedding Strategies for the Morphologically Rich and Free Phrase-Order Hungarian |
| Authors | B{'a}lint D{"o}br{"o}ssy, M{'a}rton Makrai, Bal{'a}zs Tarj{'a}n, Gy{"o}rgy Szasz{'a}k |
| Abstract | For morphologically rich languages, word embeddings provide less consistent semantic representations due to higher variance in word forms. Moreover, these languages often allow for less constrained word order, which further increases variance. For the highly agglutinative Hungarian, semantic accuracy of word embeddings measured on word analogy tasks drops by 50-75{%} compared to English. We observed that embeddings learn morphosyntax quite well instead. Therefore, we explore and evaluate several sub-word unit based embedding strategies {–} character n-grams, lemmatization provided by an NLP-pipeline, and segments obtained in unsupervised learning (morfessor) {–} to boost semantic consistency in Hungarian word vectors. The effect of changing embedding dimension and context window size have also been considered. Morphological analysis based lemmatization was found to be the best strategy to improve embeddings{'} semantic accuracy, whereas adding character n-grams was found consistently counterproductive in this regard. |
| Tasks | Lemmatization, Morphological Analysis, Word Embeddings |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-4321/ |
| https://www.aclweb.org/anthology/W19-4321 | |
| PWC | https://paperswithcode.com/paper/investigating-sub-word-embedding-strategies |
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Veritas Annotator: Discovering the Origin of a Rumour
| Title | Veritas Annotator: Discovering the Origin of a Rumour |
| Authors | Lucas Azevedo, Mohamed Moustafa |
| Abstract | Defined as the intentional or unintentionalspread of false information (K et al., 2019)through context and/or content manipulation,fake news has become one of the most seriousproblems associated with online information(Waldrop, 2017). Consequently, it comes asno surprise that Fake News Detection hasbecome one of the major foci of variousfields of machine learning and while machinelearning models have allowed individualsand companies to automate decision-basedprocesses that were once thought to be onlydoable by humans, it is no secret that thereal-life applications of such models are notviable without the existence of an adequatetraining dataset. In this paper we describethe Veritas Annotator, a web application formanually identifying the origin of a rumour.These rumours, often referred as claims,were previously checked for validity byFact-Checking Agencies. |
| Tasks | Fake News Detection, Rumour Detection |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-6614/ |
| https://www.aclweb.org/anthology/D19-6614 | |
| PWC | https://paperswithcode.com/paper/veritas-annotator-discovering-the-origin-of-a |
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Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment
| Title | Modeling Multi-mapping Relations for Precise Cross-lingual Entity Alignment |
| Authors | Xiaofei Shi, Yanghua Xiao |
| Abstract | Entity alignment aims to find entities in different knowledge graphs (KGs) that refer to the same real-world object. An effective solution for cross-lingual entity alignment is crucial for many cross-lingual AI and NLP applications. Recently many embedding-based approaches were proposed for cross-lingual entity alignment. However, almost all of them are based on TransE or its variants, which have been demonstrated by many studies to be unsuitable for encoding multi-mapping relations such as 1-N, N-1 and N-N relations, thus these methods obtain low alignment precision. To solve this issue, we propose a new embedding-based framework. Through defining dot product-based functions over embeddings, our model can better capture the semantics of both 1-1 and multi-mapping relations. We calibrate embeddings of different KGs via a small set of pre-aligned seeds. We also propose a weighted negative sampling strategy to generate valuable negative samples during training and we regard prediction as a bidirectional problem in the end. Experimental results (especially with the metric \textit{Hits@1}) on real-world multilingual datasets show that our approach significantly outperforms many other embedding-based approaches with state-of-the-art performance. |
| Tasks | Entity Alignment, Knowledge Graphs |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1075/ |
| https://www.aclweb.org/anthology/D19-1075 | |
| PWC | https://paperswithcode.com/paper/modeling-multi-mapping-relations-for-precise |
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Variational Hierarchical User-based Conversation Model
| Title | Variational Hierarchical User-based Conversation Model |
| Authors | JinYeong Bak, Alice Oh |
| Abstract | Generating appropriate conversation responses requires careful modeling of the utterances and speakers together. Some recent approaches to response generation model both the utterances and the speakers, but these approaches tend to generate responses that are overly tailored to the speakers. To overcome this limitation, we propose a new model with a stochastic variable designed to capture the speaker information and deliver it to the conversational context. An important part of this model is the network of speakers in which each speaker is connected to one or more conversational partner, and this network is then used to model the speakers better. To test whether our model generates more appropriate conversation responses, we build a new conversation corpus containing approximately 27,000 speakers and 770,000 conversations. With this corpus, we run experiments of generating conversational responses and compare our model with other state-of-the-art models. By automatic evaluation metrics and human evaluation, we show that our model outperforms other models in generating appropriate responses. An additional advantage of our model is that it generates better responses for various new user scenarios, for example when one of the speakers is a known user in our corpus but the partner is a new user. For replicability, we make available all our code and data. |
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| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1202/ |
| https://www.aclweb.org/anthology/D19-1202 | |
| PWC | https://paperswithcode.com/paper/variational-hierarchical-user-based |
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Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator
| Title | Improving Latent Alignment in Text Summarization by Generalizing the Pointer Generator |
| Authors | Xiaoyu Shen, Yang Zhao, Hui Su, Dietrich Klakow |
| Abstract | Pointer Generators have been the de facto standard for modern summarization systems. However, this architecture faces two major drawbacks: Firstly, the pointer is limited to copying the exact words while ignoring possible inflections or abstractions, which restricts its power of capturing richer latent alignment. Secondly, the copy mechanism results in a strong bias towards extractive generations, where most sentences are produced by simply copying from the source text. In this paper, we address these problems by allowing the model to {``}edit{''} pointed tokens instead of always hard copying them. The editing is performed by transforming the pointed word vector into a target space with a learned relation embedding. On three large-scale summarization dataset, we show the model is able to (1) capture more latent alignment relations than exact word matches, (2) improve word alignment accuracy, allowing for better model interpretation and controlling, (3) generate higher-quality summaries validated by both qualitative and quantitative evaluations and (4) bring more abstraction to the generated summaries. | |
| Tasks | Text Summarization, Word Alignment |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1390/ |
| https://www.aclweb.org/anthology/D19-1390 | |
| PWC | https://paperswithcode.com/paper/improving-latent-alignment-in-text |
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SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours
| Title | SemEval-2019 Task 7: RumourEval, Determining Rumour Veracity and Support for Rumours |
| Authors | Genevieve Gorrell, Elena Kochkina, Maria Liakata, Ahmet Aker, Arkaitz Zubiaga, Kalina Bontcheva, Leon Derczynski |
| Abstract | Since the first RumourEval shared task in 2017, interest in automated claim validation has greatly increased, as the danger of {``}fake news{''} has become a mainstream concern. However automated support for rumour verification remains in its infancy. It is therefore important that a shared task in this area continues to provide a focus for effort, which is likely to increase. Rumour verification is characterised by the need to consider evolving conversations and news updates to reach a verdict on a rumour{'}s veracity. As in RumourEval 2017 we provided a dataset of dubious posts and ensuing conversations in social media, annotated both for stance and veracity. The social media rumours stem from a variety of breaking news stories and the dataset is expanded to include Reddit as well as new Twitter posts. There were two concrete tasks; rumour stance prediction and rumour verification, which we present in detail along with results achieved by participants. We received 22 system submissions (a 70{%} increase from RumourEval 2017) many of which used state-of-the-art methodology to tackle the challenges involved. | |
| Tasks | Rumour Detection |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2147/ |
| https://www.aclweb.org/anthology/S19-2147 | |
| PWC | https://paperswithcode.com/paper/semeval-2019-task-7-rumoureval-determining |
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AndrejJan at SemEval-2019 Task 7: A Fusion Approach for Exploring the Key Factors pertaining to Rumour Analysis
| Title | AndrejJan at SemEval-2019 Task 7: A Fusion Approach for Exploring the Key Factors pertaining to Rumour Analysis |
| Authors | Andrej Janchevski, Sonja Gievska |
| Abstract | The viral spread of false, unverified and misleading information on the Internet has attracted a heightened attention of an interdisciplinary research community on the phenomenon. This paper contributes to the research efforts of automatically determining the veracity of rumourous tweets and classifying their replies according to stance. Our research objective was to investigate the interplay between a number of phenomenological and contextual features of rumours, in particular, we explore the extent to which network structural characteristics, metadata and user profiles could complement the linguistic analysis of the written content for the task at hand. The current findings strongly demonstrate that supplementary sources of information play significant role in classifying the veracity and the stance of Twitter interactions deemed to be rumourous. |
| Tasks | Rumour Detection |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2190/ |
| https://www.aclweb.org/anthology/S19-2190 | |
| PWC | https://paperswithcode.com/paper/andrejjan-at-semeval-2019-task-7-a-fusion |
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``When Numbers Matter!'': Detecting Sarcasm in Numerical Portions of Text
| Title | ``When Numbers Matter!'': Detecting Sarcasm in Numerical Portions of Text | |
| Authors | Abhijeet Dubey, Lakshya Kumar, Arpan Somani, Aditya Joshi, Pushpak Bhattacharyya |
| Abstract | Research in sarcasm detection spans almost a decade. However a particular form of sarcasm remains unexplored: sarcasm expressed through numbers, which we estimate, forms about 11{%} of the sarcastic tweets in our dataset. The sentence {`}Love waking up at 3 am{'} is sarcastic because of the number. In this paper, we focus on detecting sarcasm in tweets arising out of numbers. Initially, to get an insight into the problem, we implement a rule-based and a statistical machine learning-based (ML) classifier. The rule-based classifier conveys the crux of the numerical sarcasm problem, namely, incongruity arising out of numbers. The statistical ML classifier uncovers the indicators i.e., features of such sarcasm. The actual system in place, however, are two deep learning (DL) models, CNN and attention network that obtains an F-score of 0.93 and 0.91 on our dataset of tweets containing numbers. To the best of our knowledge, this is the first line of research investigating the phenomenon of sarcasm arising out of numbers, culminating in a detector thereof. | |
| Tasks | Sarcasm Detection |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/W19-1309/ |
| https://www.aclweb.org/anthology/W19-1309 | |
| PWC | https://paperswithcode.com/paper/when-numbers-matter-detecting-sarcasm-in |
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Towards Natural Language Story Understanding with Rich Logical Schemas
| Title | Towards Natural Language Story Understanding with Rich Logical Schemas |
| Authors | Gene Louis Kim, Lane Lawley, Lenhart Schubert |
| Abstract | Generating {}commonsense{'}{'} knowledge for intelligent understanding and reasoning is a difficult, long-standing problem, whose scale challenges the capacity of any approach driven primarily by human input. Furthermore, approaches based on mining statistically repetitive patterns fail to produce the rich representations humans acquire, and fall far short of human efficiency in inducing knowledge from text. The idea of our approach to this problem is to provide a learning system with a {}head start{''} consisting of a semantic parser, some basic ontological knowledge, and most importantly, a small set of very general schemas about the kinds of patterns of events (often purposive, causal, or socially conventional) that even a one- or two-year-old could reasonably be presumed to possess. We match these initial schemas to simple children{'}s stories, obtaining concrete instances, and combining and abstracting these into new candidate schemas. Both the initial and generated schemas are specified using a rich, expressive logical form. While modern approaches to schema reasoning often only use slot-and-filler structures, this logical form allows us to specify complex relations and constraints over the slots. Though formal, the representations are language-like, and as such readily relatable to NL text. The agents, objects, and other roles in the schemas are represented by typed variables, and the event variables can be related through partial temporal ordering and causal relations. To match natural language stories with existing schemas, we first parse the stories into an underspecified variant of the logical form used by the schemas, which is suitable for most concrete stories. We include a walkthrough of matching a children{'}s story to these schemas and generating inferences from these matches. |
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| Published | 2019-05-01 |
| URL | https://www.aclweb.org/anthology/W19-1102/ |
| https://www.aclweb.org/anthology/W19-1102 | |
| PWC | https://paperswithcode.com/paper/towards-natural-language-story-understanding |
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A Simple and Effective Method for Injecting Word-Level Information into Character-Aware Neural Language Models
| Title | A Simple and Effective Method for Injecting Word-Level Information into Character-Aware Neural Language Models |
| Authors | Yukun Feng, Hidetaka Kamigaito, Hiroya Takamura, Manabu Okumura |
| Abstract | We propose a simple and effective method to inject word-level information into character-aware neural language models. Unlike previous approaches which usually inject word-level information at the input of a long short-term memory (LSTM) network, we inject it into the softmax function. The resultant model can be seen as a combination of character-aware language model and simple word-level language model. Our injection method can also be used together with previous methods. Through the experiments on 14 typologically diverse languages, we empirically show that our injection method, when used together with the previous methods, works better than the previous methods, including a gating mechanism, averaging, and concatenation of word vectors. We also provide a comprehensive comparison of these injection methods. |
| Tasks | Language Modelling |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/K19-1086/ |
| https://www.aclweb.org/anthology/K19-1086 | |
| PWC | https://paperswithcode.com/paper/a-simple-and-effective-method-for-injecting |
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Group Sampling for Scale Invariant Face Detection
| Title | Group Sampling for Scale Invariant Face Detection |
| Authors | Xiang Ming, Fangyun Wei, Ting Zhang, Dong Chen, Fang Wen |
| Abstract | Detectors based on deep learning tend to detect multi-scale faces on a single input image for efficiency. Recent works, such as FPN and SSD, generally use feature maps from multiple layers with different spatial resolutions to detect objects at different scales, e.g., high-resolution feature maps for small objects. However, we find that such multi-layer prediction is not necessary. Faces at all scales can be well detected with features from a single layer of the network. In this paper, we carefully examine the factors affecting face detection across a large range of scales, and conclude that the balance of training samples, including both positive and negative ones, at different scales is the key. We propose a group sampling method which divides the anchors into several groups according to the scale, and ensure that the number of samples for each group is the same during training. Our approach using only the last layer of FPN as features is able to advance the state-of-the-arts. Comprehensive analysis and extensive experiments have been conducted to show the effectiveness of the proposed method. Our approach, evaluated on face detection benchmarks including FDDB and WIDER FACE datasets, achieves state-of-the-art results without bells and whistles. |
| Tasks | Face Detection |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Ming_Group_Sampling_for_Scale_Invariant_Face_Detection_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Ming_Group_Sampling_for_Scale_Invariant_Face_Detection_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/group-sampling-for-scale-invariant-face |
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