2026· SemEval@ACL· pp. 1477-1481· 1 citation· 9 references
Computer Science
TL;DR
NCL’s system for SemEval-2026 Task 8B, the generation track for multi-turn retrieval-augmented dialogues, achieves a harmonic mean score of 0.5973 and ranks 19th out of 26 teams on the leaderboard.
Abstract
We present NCL’s system for SemEval-2026 Task 8B, the generation track for multi-turn retrieval-augmented dialogues. Our submission follows a compact and reproducible RAG pipeline: (1) global and local question rewriting with LLM-based multi-turn relation control, (2) passage reranking with BGE-M3, (3) context-level answerability filtering with strict binary LLM judgments (“yes”/“no”), and (4) deterministic inference with a small-llm (Qwen2.5-1.5B-Instruct) plus post-generation quality fall-back (cleaning, bad-answer gate, one stricter retry, then an IDK fallback). On the official test set, our system achieved a harmonic mean score of 0.5973 (RB agg 0.4993, RL F 0.7235, RB llm 0.6105), ranking 19th out of 26 teams on the leaderboard.
Error analysis shows that a non-trivial fraction of failures are placeholder strings caused by API errors rather than incorrect generations, and that surface-level mismatches (verbosity, ortho-graphic variation) account for many of the remaining errors.
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DCASE~2026 Task~5 introduces Audio-Dependent Question Answering (ADQA), which tests whether large audio-language models answer from the audio rather than from textual priors. An Audio-Dependency Filtering (ADF) pipeline combines silent-audio probing, per-option perplexity, a large language model (LLM) commonsense check...
Haolin He, Renhe Sun, Zheqi Dai et al.· 1 citation
