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Found 10 papers, 8 papers with code
IDIAPers @ Causal News Corpus 2022: Extracting Cause-Effect-Signal Triplets via Pre-trained Autoregressive Language Model
In this paper, we describe our shared task submissions for Subtask 2 in CASE-2022, Event Causality Identification with Casual News Corpus.
IDIAPers @ Causal News Corpus 2022: Efficient Causal Relation Identification Through a Prompt-based Few-shot Approach
In this paper, we describe our participation in the subtask 1 of CASE-2022, Event Causality Identification with Casual News Corpus.
Claim-Dissector: An Interpretable Fact-Checking System with Joint Re-ranking and Veracity Prediction
We propose to disentangle the per-evidence relevance probability and its contribution to the final veracity probability in an interpretable way -- the final veracity probability is proportional to a linear ensemble of per-evidence relevance probabilities.
R2-D2: A Modular Baseline for Open-Domain Question Answering
This work presents a novel four-stage open-domain QA pipeline R2-D2 (Rank twice, reaD twice).
Ranked #2 on
Open-Domain Question Answering
on Natural Questions
Pruning the Index Contents for Memory Efficient Open-Domain QA
This work presents a novel pipeline that demonstrates what is achievable with a combined effort of state-of-the-art approaches.
NeurIPS 2020 EfficientQA Competition: Systems, Analyses and Lessons Learned
We review the EfficientQA competition from NeurIPS 2020.
Rethinking the Objectives of Extractive Question Answering
Therefore we propose multiple approaches to modelling joint probability $P(a_s, a_e)$ directly.
JokeMeter at SemEval-2020 Task 7: Convolutional humor
This paper describes our system that was designed for Humor evaluation within the SemEval-2020 Task 7.
BUT-FIT at SemEval-2020 Task 5: Automatic detection of counterfactual statements with deep pre-trained language representation models
This paper describes BUT-FIT's submission at SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals.
BUT-FIT at SemEval-2019 Task 7: Determining the Rumour Stance with Pre-Trained Deep Bidirectional Transformers
This paper describes our system submitted to SemEval 2019 Task 7: RumourEval 2019: Determining Rumour Veracity and Support for Rumours, Subtask A (Gorrell et al., 2019).
