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Improving Open-Domain Dialogue Response Generation with Multi-Source Multilingual Commonsense Knowledge
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Proposes multi-source multilingual knowledge-grounded response generation (MMKRG), a new dataset MMK-DailyDialog built by aligning XDailyDialog with ConceptNet, and MMK-BART, which uses an Estimate-Cluster-Penalize mechanism over mBART to handle cross-conflict and cross-repetition.
Problem
Knowledge-grounded dialogue response generation is limited by scarce external knowledge, especially in low-resource languages; naively merging knowledge from multiple languages causes conflicting and repetitive facts.
Approach
Retrieve commonsense facts from several language-specific ConceptNet sources, score their relevance with a cross-encoder, cluster language-agnostic SBERT embeddings with K-Means, and penalize in-cluster repeats via a rank-based factor before feeding the top facts to mBART.
Key results
- In the multilingual setting, MMK-BART (Multi-40) reaches a mean score of 12.59 versus 10.17 for uniform mBART with Multi-80 knowledge... and 11.61 for mBART with Mono-20 knowledge.
- Using all multilingual knowledge (Multi-80) with mBART performs worse than using only monolingual knowledge (Mono-20) on the mean score (11.56 vs 11.61), indicating that naive knowledge merging hurts.
- Removing the Estimate-Cluster-Penalize mechanism drops the mean score from 12.59 to 10.97 in the ablation study.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add aligned monolingual commonsense knowledge (Mono-20) | mBART and mT5 · MMK-DailyDialog monolingual | Mean: 9.64 -> 10.58 | Helped |
| Use multilingual knowledge with Estimate-Cluster-Penalize (Multi-40) | mBART-Large · MMK-DailyDialog multilingual | Mean: 11.61 -> 12.59 | Helped |
| Add multilingual knowledge directly without Estimate-Cluster-Penalize (Multi-80) | mBART-Large · MMK-DailyDialog multilingual | Mean: 11.61 -> 11.56 | Hurt |
| Use non-native cross-lingual knowledge with Estimate-Cluster-Penalize (Cross-30) | uniform mBART · MMK-DailyDialog cross-lingual, uniform models | Mean: 11.61 -> 11.79 | Helped |
| Use cross-lingual knowledge directly (Cross-60) | uniform mBART · MMK-DailyDialog cross-lingual, uniform models | Mean: 11.61 -> 10.10 | Hurt |
| Add in-cluster rank penalization (ablation) | MMK-BART · MMK-DailyDialog multilingual | Mean: 12.40 -> 12.59 | Helped |
| Add cross-encoder relevance estimation (ablation) | MMK-BART · MMK-DailyDialog multilingual | Mean: 12.14 -> 12.59 | Helped |
| Add the Estimate-Cluster-Penalize mechanism (ablation) | mBART-Large with multilingual knowledge · MMK-DailyDialog multilingual | Mean: 10.97 -> 12.59 | Helped |
| Use one shared uniform model instead of separate language models (ablation) | MMK-BART · MMK-DailyDialog multilingual | Mean: 10.55 -> 12.59 | Helped |
| Vary the number of clusters (ablation) | MMK-BART · MMK-DailyDialog multilingual | Mean | Mixed |
| Vary the penalize factor λ (ablation) | MMK-BART · MMK-DailyDialog multilingual | Mean | No clear effect |
Surprising findings
- Simply adding more multilingual knowledge (Multi-80) with mBART performs slightly worse than using only monolingual knowledge (Mono-20), showing that more knowledge is not always better.
- In the cross-lingual zero-native-knowledge setting, separate per-language mBART models using cross-lingual knowledge perform worse than with native knowledge, while MMK-BART with cross-lingual knowledge is comparable to native-knowledge mBART.
Limitations
- Relies on a translation system to create aligned dialogues in other languages and on ConceptNet retrieval heuristics, which are coarse.
- Only four languages (English, Chinese, German, Italian) evaluated; human evaluation limited to Chinese responses by native speakers.
- Penalty factor lambda shows no clear pattern in sensitivity analysis and must be tuned empirically.
Experimental setup
Datasets: XDailyDialog ConceptNet 5.5
Benchmarks: MMK-DailyDialog
Metrics: BERT embedding score F1 ROUGE-L BLEU-1 BLEU-2 BLEU-3 BLEU-4 DIST-1 DIST-2 Entropy-4
Baselines: mBART mT5
Builds on: mBART XDailyDialog ConceptNet SentenceBERT
Closest papers by meaning
- Zero-Resource Knowledge-Grounded Dialogue Generation (NeurIPS 2020 · similarity 0.90)
- KPT: Keyword-Guided Pre-training for Grounded Dialog Generation (AAAI 2023 · similarity 0.90)
- From Retrieval to Generation: A Simple and Unified Generative Model for End-to-End Task-Oriented Dialogue (AAAI 2024 · similarity 0.87)
- CORECODE: A Common Sense Annotated Dialogue Dataset with Benchmark Tasks for Chinese Large Language Models (AAAI 2024 · similarity 0.86)
- SD-Eval: A Benchmark Dataset for Spoken Dialogue Understanding Beyond Words (NeurIPS 2024 · similarity 0.86)
- DialectLLM: A Dialect-Aware Dialog[ue] Generation Framework Beyond Standard American English (NeurIPS 2026 · similarity 0.86)
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