20 research outputs found
Overview of the CLEF-2022 CheckThat! Lab Task 1 on Identifying Relevant Claims in Tweets
We present an overview of CheckThat! lab 2022 Task 1, part of the 2022 Conference and Labs of the Evaluation Forum (CLEF). Task 1 asked to predict which posts in a Twitter stream are worth fact-checking, focusing on COVID-19 and politics in six languages: Arabic, Bulgarian, Dutch, English, Spanish, and Turkish. A total of 19 teams participated and most submissions managed to achieve sizable improvements over the baselines using Transformer-based models such as BERT and GPT-3. Across the four subtasks, approaches that targetted multiple languages (be it individually or in conjunction, in general obtained the best performance. We describe the dataset and the task setup, including the evaluation settings, and we give a brief overview of the participating systems. As usual in the CheckThat! lab, we release to the research community all datasets from the lab as well as the evaluation scripts, which should enable further research on finding relevant tweets that can help different stakeholders such as fact-checkers, journalists, and policymakers
CheckThat! at CLEF 2020: Enabling the Automatic Identification and Verification of Claims in Social Media
We describe the third edition of the CheckThat! Lab, which is part of the
2020 Cross-Language Evaluation Forum (CLEF). CheckThat! proposes four
complementary tasks and a related task from previous lab editions, offered in
English, Arabic, and Spanish. Task 1 asks to predict which tweets in a Twitter
stream are worth fact-checking. Task 2 asks to determine whether a claim posted
in a tweet can be verified using a set of previously fact-checked claims. Task
3 asks to retrieve text snippets from a given set of Web pages that would be
useful for verifying a target tweet's claim. Task 4 asks to predict the
veracity of a target tweet's claim using a set of Web pages and potentially
useful snippets in them. Finally, the lab offers a fifth task that asks to
predict the check-worthiness of the claims made in English political debates
and speeches. CheckThat! features a full evaluation framework. The evaluation
is carried out using mean average precision or precision at rank k for ranking
tasks, and F1 for classification tasks.Comment: Computational journalism, Check-worthiness, Fact-checking, Veracity,
CLEF-2020 CheckThat! La
Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media
We present an overview of the third edition of the CheckThat! Lab at CLEF
2020. The lab featured five tasks in two different languages: English and
Arabic. The first four tasks compose the full pipeline of claim verification in
social media: Task 1 on check-worthiness estimation, Task 2 on retrieving
previously fact-checked claims, Task 3 on evidence retrieval, and Task 4 on
claim verification. The lab is completed with Task 5 on check-worthiness
estimation in political debates and speeches. A total of 67 teams registered to
participate in the lab (up from 47 at CLEF 2019), and 23 of them actually
submitted runs (compared to 14 at CLEF 2019). Most teams used deep neural
networks based on BERT, LSTMs, or CNNs, and achieved sizable improvements over
the baselines on all tasks. Here we describe the tasks setup, the evaluation
results, and a summary of the approaches used by the participants, and we
discuss some lessons learned. Last but not least, we release to the research
community all datasets from the lab as well as the evaluation scripts, which
should enable further research in the important tasks of check-worthiness
estimation and automatic claim verification.Comment: Check-Worthiness Estimation, Fact-Checking, Veracity, Evidence-based
Verification, Detecting Previously Fact-Checked Claims, Social Media
Verification, Computational Journalism, COVID-1
Semantic Representation and Inference for NLP
Semantic representation and inference is essential for Natural Language
Processing (NLP). The state of the art for semantic representation and
inference is deep learning, and particularly Recurrent Neural Networks (RNNs),
Convolutional Neural Networks (CNNs), and transformer Self-Attention models.
This thesis investigates the use of deep learning for novel semantic
representation and inference, and makes contributions in the following three
areas: creating training data, improving semantic representations and extending
inference learning. In terms of creating training data, we contribute the
largest publicly available dataset of real-life factual claims for the purpose
of automatic claim verification (MultiFC), and we present a novel inference
model composed of multi-scale CNNs with different kernel sizes that learn from
external sources to infer fact checking labels. In terms of improving semantic
representations, we contribute a novel model that captures non-compositional
semantic indicators. By definition, the meaning of a non-compositional phrase
cannot be inferred from the individual meanings of its composing words (e.g.,
hot dog). Motivated by this, we operationalize the compositionality of a phrase
contextually by enriching the phrase representation with external word
embeddings and knowledge graphs. Finally, in terms of inference learning, we
propose a series of novel deep learning architectures that improve inference by
using syntactic dependencies, by ensembling role guided attention heads,
incorporating gating layers, and concatenating multiple heads in novel and
effective ways. This thesis consists of seven publications (five published and
two under review).Comment: PhD thesis, the University of Copenhage