23 research outputs found
STANCY: Stance Classification Based on Consistency Cues
Controversial claims are abundant in online media and discussion forums. A better understanding of such claims requires analyzing them from different perspectives. Stance classification is a necessary step for inferring these perspectives in terms of supporting or opposing the claim. In this work, we present a neural network model for stance classification leveraging BERT representations and augmenting them with a novel consistency constraint. Experiments on the Perspectrum dataset, consisting of claims and users' perspectives from various debate websites, demonstrate the effectiveness of our approach over state-of-the-art baselines
STANCY: Stance Classification Based on Consistency Cues
Controversial claims are abundant in online media and discussion forums. A
better understanding of such claims requires analyzing them from different
perspectives. Stance classification is a necessary step for inferring these
perspectives in terms of supporting or opposing the claim. In this work, we
present a neural network model for stance classification leveraging BERT
representations and augmenting them with a novel consistency constraint.
Experiments on the Perspectrum dataset, consisting of claims and users'
perspectives from various debate websites, demonstrate the effectiveness of our
approach over state-of-the-art baselines.Comment: Accepted at EMNLP 201
Commonsense knowledge enhanced memory network for stance classification
Stance classification aims at identifying, in the text, the attitude toward the given targets as favorable, negative, or unrelated. In existing models for stance classification, only textual representation is leveraged, while commonsense knowledge is ignored. In order to better incorporate commonsense knowledge into stance classification, we propose a novel model named commonsense knowledge enhanced memory network, which jointly represents textual and commonsense knowledge representation of given target and text. The textual memory module in our model treats the textual representation as memory vectors, and uses attention mechanism to embody the important parts. For commonsense knowledge memory module, we jointly leverage the entity and relation embeddings learned by TransE model to take full advantage of constraints of the knowledge graph. Experimental results on the SemEval dataset show that the combination of the commonsense knowledge memory and textual memory can improve stance classification
Stance Detection in Web and Social Media: A Comparative Study
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
X-stance: A Multilingual Multi-Target Dataset for Stance Detection
We extract a large-scale stance detection dataset from comments written by
candidates of elections in Switzerland. The dataset consists of German, French
and Italian text, allowing for a cross-lingual evaluation of stance detection.
It contains 67 000 comments on more than 150 political issues (targets). Unlike
stance detection models that have specific target issues, we use the dataset to
train a single model on all the issues. To make learning across targets
possible, we prepend to each instance a natural question that represents the
target (e.g. "Do you support X?"). Baseline results from multilingual BERT show
that zero-shot cross-lingual and cross-target transfer of stance detection is
moderately successful with this approach.Comment: SwissText + KONVENS 2020. Data and code are available at
https://github.com/ZurichNLP/xstanc