New Benchmark and Methods Improve Parsing of Spoken Bilingual Conversations
Researchers introduced SpokeBench, a new benchmark dataset for English-Spanish spoken conversations, along with improved evaluation methods and a parsing framework designed to handle the complexities of natural speech. Spoken bilingual conversations contain disfluencies and discourse patterns that challenge standard syntactic parsing approaches. The work addresses a gap in natural language processing by providing tools specifically designed for conversational speech rather than written text.
A new study presents SpokeBench, an expert-annotated benchmark for evaluating syntactic parsing of spoken English-Spanish bilingual conversations. The researchers identified that standard Universal Dependencies (UD) assumptions and evaluation metrics are poorly suited for natural speech, which includes disfluencies and discourse-driven structures. To address this, they developed Flex-UD, an evaluation metric that distinguishes between catastrophic parsing failures and linguistically acceptable variations, and DECAP, a decoupled parsing framework that separates the handling of spoken phenomena from core syntactic analysis. Testing across both proprietary and open-weight large language models showed DECAP achieved over 60% improvements in UPOS-F1 scores compared to baselines, with additional gains revealed by the new evaluation metric that standard metrics would miss.
What's missing
The study does not discuss computational efficiency or inference time comparisons between DECAP and baseline approaches. Additionally, the paper does not address how the methods generalize to other language pairs beyond English-Spanish or to other spoken language phenomena such as code-switching patterns.
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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