New Benchmark and Methods Improve Parsing of Spoken Bilingual Conversations
Researchers have released SpokeBench, an expert-annotated English-Spanish benchmark, along with a new parsing framework called DECAP and an evaluation metric called Flex-UD, all aimed at improving syntactic analysis of spoken bilingual conversations. Standard Universal Dependencies (UD) tools struggle with the disfluencies, code-switching, and discourse-driven structures common in real spoken dialogue. The work addresses a significant gap in NLP infrastructure for handling naturalistic multilingual speech.
A team of researchers has published a study on arXiv introducing three interconnected contributions to the field of computational linguistics. SpokeBench is an expert-annotated benchmark dataset of English-Spanish bilingual conversations designed to capture structurally complex spoken language phenomena such as disfluencies and code-switching. The authors also propose Flex-UD, an ambiguity-aware evaluation metric intended to distinguish catastrophic parsing errors from linguistically acceptable structural variations—a distinction that standard attachment-based metrics obscure. Their third contribution, DECAP, is a decoupled agentic parsing framework that separates the handling of spoken-language phenomena from core syntactic analysis, allowing robust dependency parsing without requiring model retraining. Experiments conducted across both proprietary and open-weight large language models show DECAP achieves over 60% improvement in UPOS-F1 Score compared to baselines. The Flex-UD metric further reveals performance gains that would otherwise go undetected under conventional evaluation protocols.
What's missing
The study is a preprint and has not yet undergone formal peer review. It is also unclear how DECAP performs on other language pairs beyond English-Spanish, or how computational costs compare to baseline approaches.
What different sources said
- arXiv cs.CLCenter
Lost in Speech: Benchmarking, Evaluation, and Parsing of Spoken Bilingual Conversational Language Beyond Standard UD Assumptions
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