599 research outputs found
A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection
The truth is significantly hampered by massive rumors that spread along with
breaking news or popular topics. Since there is sufficient corpus gathered from
the same domain for model training, existing rumor detection algorithms show
promising performance on yesterday's news. However, due to a lack of training
data and prior expert knowledge, they are poor at spotting rumors concerning
unforeseen events, especially those propagated in different languages (i.e.,
low-resource regimes). In this paper, we propose a unified contrastive transfer
framework to detect rumors by adapting the features learned from well-resourced
rumor data to that of the low-resourced. More specifically, we first represent
rumor circulated on social media as an undirected topology, and then train a
Multi-scale Graph Convolutional Network via a unified contrastive paradigm. Our
model explicitly breaks the barriers of the domain and/or language issues, via
language alignment and a novel domain-adaptive contrastive learning mechanism.
To enhance the representation learning from a small set of target events, we
reveal that rumor-indicative signal is closely correlated with the uniformity
of the distribution of these events. We design a target-wise contrastive
training mechanism with three data augmentation strategies, capable of unifying
the representations by distinguishing target events. Extensive experiments
conducted on four low-resource datasets collected from real-world microblog
platforms demonstrate that our framework achieves much better performance than
state-of-the-art methods and exhibits a superior capacity for detecting rumors
at early stages.Comment: A significant extension of the first contrastive approach for
low-resource rumor detection (arXiv:2204.08143
CL-XABSA: Contrastive Learning for Cross-lingual Aspect-based Sentiment Analysis
As an extensive research in the field of Natural language processing (NLP),
aspect-based sentiment analysis (ABSA) is the task of predicting the sentiment
expressed in a text relative to the corresponding aspect. Unfortunately, most
languages lack of sufficient annotation resources, thus more and more recent
researchers focus on cross-lingual aspect-based sentiment analysis (XABSA).
However, most recent researches only concentrate on cross-lingual data
alignment instead of model alignment. To this end, we propose a novel
framework, CL-XABSA: Contrastive Learning for Cross-lingual Aspect-Based
Sentiment Analysis. Specifically, we design two contrastive strategies, token
level contrastive learning of token embeddings (TL-CTE) and sentiment level
contrastive learning of token embeddings (SL-CTE), to regularize the semantic
space of source and target language to be more uniform. Since our framework can
receive datasets in multiple languages during training, our framework can be
adapted not only for XABSA task, but also for multilingual aspect-based
sentiment analysis (MABSA). To further improve the performance of our model, we
perform knowledge distillation technology leveraging data from unlabeled target
language. In the distillation XABSA task, we further explore the comparative
effectiveness of different data (source dataset, translated dataset, and
code-switched dataset). The results demonstrate that the proposed method has a
certain improvement in the three tasks of XABSA, distillation XABSA and MABSA.
For reproducibility, our code for this paper is available at
https://github.com/GKLMIP/CL-XABSA
Zero-shot stance detection based on cross-domain feature enhancement by contrastive learning
Zero-shot stance detection is challenging because it requires detecting the
stance of previously unseen targets in the inference phase. The ability to
learn transferable target-invariant features is critical for zero-shot stance
detection. In this work, we propose a stance detection approach that can
efficiently adapt to unseen targets, the core of which is to capture
target-invariant syntactic expression patterns as transferable knowledge.
Specifically, we first augment the data by masking the topic words of
sentences, and then feed the augmented data to an unsupervised contrastive
learning module to capture transferable features. Then, to fit a specific
target, we encode the raw texts as target-specific features. Finally, we adopt
an attention mechanism, which combines syntactic expression patterns with
target-specific features to obtain enhanced features for predicting previously
unseen targets. Experiments demonstrate that our model outperforms competitive
baselines on four benchmark datasets
Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection
Stance Detection is concerned with identifying the attitudes expressed by an
author towards a target of interest. This task spans a variety of domains
ranging from social media opinion identification to detecting the stance for a
legal claim. However, the framing of the task varies within these domains, in
terms of the data collection protocol, the label dictionary and the number of
available annotations. Furthermore, these stance annotations are significantly
imbalanced on a per-topic and inter-topic basis. These make multi-domain stance
detection a challenging task, requiring standardization and domain adaptation.
To overcome this challenge, we propose opic fficient
anc etection (TESTED), consisting of a
topic-guided diversity sampling technique and a contrastive objective that is
used for fine-tuning a stance classifier. We evaluate the method on an existing
benchmark of datasets with in-domain, i.e. all topics seen and
out-of-domain, i.e. unseen topics, experiments. The results show that our
method outperforms the state-of-the-art with an average of F1 points
increase in-domain, and is more generalizable with an averaged increase of
F1 on out-of-domain evaluation while using of the training
data. We show that our sampling technique mitigates both inter- and per-topic
class imbalances. Finally, our analysis demonstrates that the contrastive
learning objective allows the model a more pronounced segmentation of samples
with varying labels.Comment: ACL 2023 (Oral
TATA: Stance Detection via Topic-Agnostic and Topic-Aware Embeddings
Stance detection is important for understanding different attitudes and
beliefs on the Internet. However, given that a passage's stance toward a given
topic is often highly dependent on that topic, building a stance detection
model that generalizes to unseen topics is difficult. In this work, we propose
using contrastive learning as well as an unlabeled dataset of news articles
that cover a variety of different topics to train topic-agnostic/TAG and
topic-aware/TAW embeddings for use in downstream stance detection. Combining
these embeddings in our full TATA model, we achieve state-of-the-art
performance across several public stance detection datasets (0.771 -score
on the Zero-shot VAST dataset). We release our code and data at
https://github.com/hanshanley/tata.Comment: Accepted to EMNLP 2023; Updated citation
Capturing stance dynamics in social media: open challenges and research directions
Social media platforms provide a goldmine for mining public opinion on issues
of wide societal interest and impact. Opinion mining is a problem that can be
operationalised by capturing and aggregating the stance of individual social
media posts as supporting, opposing or being neutral towards the issue at hand.
While most prior work in stance detection has investigated datasets that cover
short periods of time, interest in investigating longitudinal datasets has
recently increased. Evolving dynamics in linguistic and behavioural patterns
observed in new data require adapting stance detection systems to deal with the
changes. In this survey paper, we investigate the intersection between
computational linguistics and the temporal evolution of human communication in
digital media. We perform a critical review of emerging research considering
dynamics, exploring different semantic and pragmatic factors that impact
linguistic data in general, and stance in particular. We further discuss
current directions in capturing stance dynamics in social media. We discuss the
challenges encountered when dealing with stance dynamics, identify open
challenges and discuss future directions in three key dimensions: utterance,
context and influence
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