@inproceedings{shallouf-etal-2024-cam-2,
title = "{CAM} 2.0: End-to-End Open Domain Comparative Question Answering System",
author = "Shallouf, Ahmad and
Herasimchyk, Hanna and
Salnikov, Mikhail and
Garrido Veliz, Rudy Alexandro and
Mestvirishvili, Natia and
Panchenko, Alexander and
Biemann, Chris and
Nikishina, Irina",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.238",
pages = "2657--2672",
abstract = "Comparative Question Answering (CompQA) is a Natural Language Processing task that combines Question Answering and Argument Mining approaches to answer subjective comparative questions in an efficient argumentative manner. In this paper, we present an end-to-end (full pipeline) system for answering comparative questions called CAM 2.0 as well as a public leaderboard called CompUGE that unifies the existing datasets under a single easy-to-use evaluation suite. As compared to previous web-form-based CompQA systems, it features question identification, object and aspect labeling, stance classification, and summarization using up-to-date models. We also select the most time- and memory-effective pipeline by comparing separately fine-tuned Transformer Encoder models which show state-of-the-art performance on the subtasks with Generative LLMs in few-shot and LoRA setups. We also conduct a user study for a whole-system evaluation.",
}
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%0 Conference Proceedings
%T CAM 2.0: End-to-End Open Domain Comparative Question Answering System
%A Shallouf, Ahmad
%A Herasimchyk, Hanna
%A Salnikov, Mikhail
%A Garrido Veliz, Rudy Alexandro
%A Mestvirishvili, Natia
%A Panchenko, Alexander
%A Biemann, Chris
%A Nikishina, Irina
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F shallouf-etal-2024-cam-2
%X Comparative Question Answering (CompQA) is a Natural Language Processing task that combines Question Answering and Argument Mining approaches to answer subjective comparative questions in an efficient argumentative manner. In this paper, we present an end-to-end (full pipeline) system for answering comparative questions called CAM 2.0 as well as a public leaderboard called CompUGE that unifies the existing datasets under a single easy-to-use evaluation suite. As compared to previous web-form-based CompQA systems, it features question identification, object and aspect labeling, stance classification, and summarization using up-to-date models. We also select the most time- and memory-effective pipeline by comparing separately fine-tuned Transformer Encoder models which show state-of-the-art performance on the subtasks with Generative LLMs in few-shot and LoRA setups. We also conduct a user study for a whole-system evaluation.
%U https://aclanthology.org/2024.lrec-main.238
%P 2657-2672
Markdown (Informal)
[CAM 2.0: End-to-End Open Domain Comparative Question Answering System](https://aclanthology.org/2024.lrec-main.238) (Shallouf et al., LREC-COLING 2024)
ACL
- Ahmad Shallouf, Hanna Herasimchyk, Mikhail Salnikov, Rudy Alexandro Garrido Veliz, Natia Mestvirishvili, Alexander Panchenko, Chris Biemann, and Irina Nikishina. 2024. CAM 2.0: End-to-End Open Domain Comparative Question Answering System. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 2657–2672, Torino, Italia. ELRA and ICCL.