Get our free extension to see links to code for papers anywhere online!

Chrome logo Add to Chrome

Firefox logo Add to Firefox

"Topic": models, code, and papers

Stance Detection in Web and Social Media: A Comparative Study

Jul 12, 2020
Shalmoli Ghosh, Prajwal Singhania, Siddharth Singh, Koustav Rudra, Saptarshi Ghosh

Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances. Several methodologies for automatic stance detection from text have been proposed in literature. To our knowledge, there has not been any systematic investigation towards their reproducibility, and their comparative performances. In this work, we explore the reproducibility of several existing stance detection models, including both neural models and classical classifier-based models. Through experiments on two datasets -- (i)~the popular SemEval microblog dataset, and (ii)~a set of health-related online news articles -- we also perform a detailed comparative analysis of various methods and explore their shortcomings. Implementations of all algorithms discussed in this paper are available at https://github.com/prajwal1210/Stance-Detection-in-Web-and-Social-Media.

* Proceedings of Conference and Labs of the Evaluation Forum (CLEF) 2019; Lecture Notes in Computer Science, vol 11696, pp. 75-87 

  Access Paper or Ask Questions

Modeling Discourse Structure for Document-level Neural Machine Translation

Jun 08, 2020
Junxuan Chen, Xiang Li, Jiarui Zhang, Chulun Zhou, Jianwei Cui, Bin Wang, Jinsong Su

Recently, document-level neural machine translation (NMT) has become a hot topic in the community of machine translation. Despite its success, most of existing studies ignored the discourse structure information of the input document to be translated, which has shown effective in other tasks. In this paper, we propose to improve document-level NMT with the aid of discourse structure information. Our encoder is based on a hierarchical attention network (HAN). Specifically, we first parse the input document to obtain its discourse structure. Then, we introduce a Transformer-based path encoder to embed the discourse structure information of each word. Finally, we combine the discourse structure information with the word embedding before it is fed into the encoder. Experimental results on the English-to-German dataset show that our model can significantly outperform both Transformer and Transformer+HAN.


  Access Paper or Ask Questions

Adversarial Attacks and Defense on Texts: A Survey

May 31, 2020
Aminul Huq, Mst. Tasnim Pervin

Deep leaning models have been used widely for various purposes in recent years in object recognition, self-driving cars, face recognition, speech recognition, sentiment analysis and many others. However, in recent years it has been shown that these models possess weakness to noises which forces the model to misclassify. This issue has been studied profoundly in image and audio domain. Very little has been studied on this issue with respect to textual data. Even less survey on this topic has been performed to understand different types of attacks and defense techniques. In this manuscript we accumulated and analyzed different attacking techniques, various defense models on how to overcome this issue in order to provide a more comprehensive idea. Later we point out some of the interesting findings of all papers and challenges that need to be overcome in order to move forward in this field.


  Access Paper or Ask Questions

Adversarial Attacks and Defense on Textual Data: A Review

May 28, 2020
Aminul Huq, Mst. Tasnim Pervin

Deep leaning models have been used widely for various purposes in recent years in object recognition, self-driving cars, face recognition, speech recognition, sentiment analysis and many others. However, in recent years it has been shown that these models possess weakness to noises which forces the model to misclassify. This issue has been studied profoundly in image and audio domain. Very little has been studied on this issue with respect to textual data. Even less survey on this topic has been performed to understand different types of attacks and defense techniques. In this manuscript we accumulated and analyzed different attacking techniques, various defense models on how to overcome this issue in order to provide a more comprehensive idea. Later we point out some of the interesting findings of all papers and challenges that need to be overcome in order to move forward in this field.


  Access Paper or Ask Questions

Ring Reservoir Neural Networks for Graphs

May 11, 2020
Claudio Gallicchio, Alessio Micheli

Machine Learning for graphs is nowadays a research topic of consolidated relevance. Common approaches in the field typically resort to complex deep neural network architectures and demanding training algorithms, highlighting the need for more efficient solutions. The class of Reservoir Computing (RC) models can play an important role in this context, enabling to develop fruitful graph embeddings through untrained recursive architectures. In this paper, we study progressive simplifications to the design strategy of RC neural networks for graphs. Our core proposal is based on shaping the organization of the hidden neurons to follow a ring topology. Experimental results on graph classification tasks indicate that ring-reservoirs architectures enable particularly effective network configurations, showing consistent advantages in terms of predictive performance.

* Accepted for IJCNN/WCCI 2020 

  Access Paper or Ask Questions

Augmenting Transformers with KNN-Based Composite Memory for Dialogue

Apr 27, 2020
Angela Fan, Claire Gardent, Chloe Braud, Antoine Bordes

Various machine learning tasks can benefit from access to external information of different modalities, such as text and images. Recent work has focused on learning architectures with large memories capable of storing this knowledge. We propose augmenting generative Transformer neural networks with KNN-based Information Fetching (KIF) modules. Each KIF module learns a read operation to access fixed external knowledge. We apply these modules to generative dialogue modeling, a challenging task where information must be flexibly retrieved and incorporated to maintain the topic and flow of conversation. We demonstrate the effectiveness of our approach by identifying relevant knowledge from Wikipedia, images, and human-written dialogue utterances, and show that leveraging this retrieved information improves model performance, measured by automatic and human evaluation.


  Access Paper or Ask Questions

Using natural language processing to extract health-related causality from Twitter messages

Nov 15, 2019
Son Doan, Elly W Yang, Sameer Tilak, Manabu Torii

Twitter messages (tweets) contain various types of information, which include health-related information. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily life. In this work, we evaluated an approach to extracting causal relations from tweets using natural language processing (NLP) techniques. We focused on three health-related topics: stress", "insomnia", and "headache". We proposed a set of lexico-syntactic patterns based on dependency parser outputs to extract causal information. A large dataset consisting of 24 million tweets were used. The results show that our approach achieved an average precision between 74.59% and 92.27%. Analysis of extracted relations revealed interesting findings about health-related in Twitter.

* 2018 IEEE International Conference on Healthcare Informatics Workshop 
* 5 pages 

  Access Paper or Ask Questions

CodeSwitch-Reddit: Exploration of Written Multilingual Discourse in Online Discussion Forums

Aug 30, 2019
Ella Rabinovich, Masih Sultani, Suzanne Stevenson

In contrast to many decades of research on oral code-switching, the study of written multilingual productions has only recently enjoyed a surge of interest. Many open questions remain regarding the sociolinguistic underpinnings of written code-switching, and progress has been limited by a lack of suitable resources. We introduce a novel, large, and diverse dataset of written code-switched productions, curated from topical threads of multiple bilingual communities on the Reddit discussion platform, and explore questions that were mainly addressed in the context of spoken language thus far. We investigate whether findings in oral code-switching concerning content and style, as well as speaker proficiency, are carried over into written code-switching in discussion forums. The released dataset can further facilitate a range of research and practical activities.

* EMNLP2019, 11 pages 

  Access Paper or Ask Questions

Classification and Clustering of Arguments with Contextualized Word Embeddings

Jun 24, 2019
Nils Reimers, Benjamin Schiller, Tilman Beck, Johannes Daxenberger, Christian Stab, Iryna Gurevych

We experiment with two recent contextualized word embedding methods (ELMo and BERT) in the context of open-domain argument search. For the first time, we show how to leverage the power of contextualized word embeddings to classify and cluster topic-dependent arguments, achieving impressive results on both tasks and across multiple datasets. For argument classification, we improve the state-of-the-art for the UKP Sentential Argument Mining Corpus by 20.8 percentage points and for the IBM Debater - Evidence Sentences dataset by 7.4 percentage points. For the understudied task of argument clustering, we propose a pre-training step which improves by 7.8 percentage points over strong baselines on a novel dataset, and by 12.3 percentage points for the Argument Facet Similarity (AFS) Corpus.

* Conference paper at ACL 2019 

  Access Paper or Ask Questions

Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding

Jun 11, 2019
Junyi Li, Wayne Xin Zhao, Ji-Rong Wen, Yang Song

Generating long and informative review text is a challenging natural language generation task. Previous work focuses on word-level generation, neglecting the importance of topical and syntactic characteristics from natural languages. In this paper, we propose a novel review generation model by characterizing an elaborately designed aspect-aware coarse-to-fine generation process. First, we model the aspect transitions to capture the overall content flow. Then, to generate a sentence, an aspect-aware sketch will be predicted using an aspect-aware decoder. Finally, another decoder fills in the semantic slots by generating corresponding words. Our approach is able to jointly utilize aspect semantics, syntactic sketch, and context information. Extensive experiments results have demonstrated the effectiveness of the proposed model.

* 11 pages, 2 figures, 7 tables 

  Access Paper or Ask Questions

<<
253
254
255
256
257
258
259
260
261
262
263
264
265
>>