AMEX AI Labs at SemEval-2022 Task 10: Contextualized fine-tuning of BERT for Structured Sentiment Analysis
Pratyush Sarangi, Shamika Ganesan, Piyush Arora, Salil Joshi
Abstract
We describe the work carried out by AMEX AI Labs on the structured sentiment analysis task at SemEval-2022. This task focuses on extracting fine grained information w.r.t. to source, target and polar expressions in a given text. We propose a BERT based encoder, which utilizes a novel concatenation mechanism for combining syntactic and pretrained embeddings with BERT embeddings. Our system achieved an average rank of 14/32 systems, based on the average scores across seven datasets for five languages provided for the monolingual task. The proposed BERT based approaches outperformed BiLSTM based approaches used for structured sentiment extraction problem. We provide an in-depth analysis based on our post submission analysis.- Anthology ID:
- 2022.semeval-1.181
- Volume:
- Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Editors:
- Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1296–1304
- Language:
- URL:
- https://aclanthology.org/2022.semeval-1.181
- DOI:
- 10.18653/v1/2022.semeval-1.181
- Cite (ACL):
- Pratyush Sarangi, Shamika Ganesan, Piyush Arora, and Salil Joshi. 2022. AMEX AI Labs at SemEval-2022 Task 10: Contextualized fine-tuning of BERT for Structured Sentiment Analysis. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pages 1296–1304, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- AMEX AI Labs at SemEval-2022 Task 10: Contextualized fine-tuning of BERT for Structured Sentiment Analysis (Sarangi et al., SemEval 2022)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2022.semeval-1.181.pdf
- Data
- MPQA Opinion Corpus