✅ How Robust are Fact Checking Systems on Colloquial Claims?. In NAACL-HLT, 2021.

Byeongchang Kim

Last update: Mar 15, 2022

Related tags

Deep Learning colloquial-claims

Overview

How Robust are Fact Checking Systems
on Colloquial Claims?

Official PyTorch implementation of our NAACL paper:
Byeongchang Kim*, Hyunwoo Kim*, Seokhee Hong, and Gunhee Kim. How Robust are Fact Checking Systems on Colloquial Claims? NAACL-HLT, 2021 [Paper] (* equal contribution)

If you use the materials in this repository as part of any published research, we ask you to cite the following paper:

@inproceedings{Kim:2021:colloquial,
  title={How Robust are Fact Checking Systems on Colloquial Claims?},
  author={Kim, Byeongchang and Kim, Hyunwoo and Hong, Seokhee and Kim, Gunhee},
  booktitle={NAACL-HLT},
  year={2021}
}

Colloquial Claims dataset

You can download the paper version of our Colloquial Claims dataset via following urls:
[train] [valid] [test]

You can read and explore the dataset as follows:

import json

turns = []
with open('colloquial_claims_train.jsonl', 'r') as fp:
    for line in fp:
        turns.append(json.loads(line))

print(turns[0].keys())
# dict_keys(['colloquial_claims', 'fever_claim', 'fever_label', 'evidences', 'gold_evidence_set', 'fever_id'])

Running style transfer pipeline

In progress

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Comments

pipeline 관련 문의드립니다.

안녕하세요. 먼저 발표하신 논문에서의 결과를 흥미롭게 봤습니다. 논문에서 FEVER에 사용하신 파이프라인을 다양한 데이터셋에도 한번 적용해서 실험을 해보려고 합니다.

Readme에 style-transfer 파이프라인을 공개 예정이라고 돼있는데, 혹시 언제쯤 예정이신지 궁금해서 문의드립니다.

혹시 issue가 불편하시면, [email protected]으로 알려주시면 감사하겠습니다

opened by teddy309 0

✅ How Robust are Fact Checking Systems on Colloquial Claims?. In NAACL-HLT, 2021.

Related tags

Overview

How Robust are Fact Checking Systems
on Colloquial Claims?

Colloquial Claims dataset

Running style transfer pipeline

You might also like...

Code for NAACL 2021 full paper "Efficient Attentions for Long Document Summarization"

Paddle implementation for "Highly Efficient Knowledge Graph Embedding Learning with Closed-Form Orthogonal Procrustes Analysis" (NAACL 2021)

Paddle implementation for "Cross-Lingual Word Embedding Refinement by ℓ1 Norm Optimisation" (NAACL 2021)

Source code for NAACL 2021 paper "TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference"

Open-Ended Commonsense Reasoning (NAACL 2021)

Pytorch implementation of Supporting Clustering with Contrastive Learning, NAACL 2021

Official repository with code and data accompanying the NAACL 2021 paper "Hurdles to Progress in Long-form Question Answering" (https://arxiv.org/abs/2103.06332).

Adversarial-Information-Bottleneck - Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck (NeurIPS21)

Code for Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022)

Comments

pipeline 관련 문의드립니다.

Owner

Byeongchang Kim

Codes for ACL-IJCNLP 2021 Paper "Zero-shot Fact Verification by Claim Generation"

Contrastive Fact Verification

source code and pre-trained/fine-tuned checkpoint for NAACL 2021 paper LightningDOT

Codes for NAACL 2021 Paper "Unsupervised Multi-hop Question Answering by Question Generation"

Official code of our work, Unified Pre-training for Program Understanding and Generation [NAACL 2021].

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[NAACL & ACL 2021] SapBERT: Self-alignment pretraining for BERT.

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✅ How Robust are Fact Checking Systems on Colloquial Claims?. In NAACL-HLT, 2021.

Related tags

Overview

How Robust are Fact Checking Systemson Colloquial Claims?

Colloquial Claims dataset

Running style transfer pipeline

You might also like...

Code for NAACL 2021 full paper "Efficient Attentions for Long Document Summarization"

Paddle implementation for "Highly Efficient Knowledge Graph Embedding Learning with Closed-Form Orthogonal Procrustes Analysis" (NAACL 2021)

Paddle implementation for "Cross-Lingual Word Embedding Refinement by ℓ1 Norm Optimisation" (NAACL 2021)

Source code for NAACL 2021 paper "TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference"

Open-Ended Commonsense Reasoning (NAACL 2021)

Pytorch implementation of Supporting Clustering with Contrastive Learning, NAACL 2021

Official repository with code and data accompanying the NAACL 2021 paper "Hurdles to Progress in Long-form Question Answering" (https://arxiv.org/abs/2103.06332).

Adversarial-Information-Bottleneck - Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck (NeurIPS21)

Code for Private Recommender Systems: How Can Users Build Their Own Fair Recommender Systems without Log Data? (SDM 2022)

Comments

pipeline 관련 문의드립니다.

Owner

Byeongchang Kim

Codes for ACL-IJCNLP 2021 Paper "Zero-shot Fact Verification by Claim Generation"

Contrastive Fact Verification

source code and pre-trained/fine-tuned checkpoint for NAACL 2021 paper LightningDOT

Codes for NAACL 2021 Paper "Unsupervised Multi-hop Question Answering by Question Generation"

Official code of our work, Unified Pre-training for Program Understanding and Generation [NAACL 2021].

Designing a Minimal Retrieve-and-Read System for Open-Domain Question Answering (NAACL 2021)

NAACL'2021: Factual Probing Is [MASK]: Learning vs. Learning to Recall

Contextualized Perturbation for Textual Adversarial Attack, NAACL 2021

[NAACL & ACL 2021] SapBERT: Self-alignment pretraining for BERT.

Self-training with Weak Supervision (NAACL 2021)

How Robust are Fact Checking Systems
on Colloquial Claims?