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    <abstract>Vision-language models (VLMs) have shown to be effective at image retrieval based on simple text queries, but text-image retrieval based on conversational input remains a challenge. Consequently, if we want to use VLMs for reference resolution in visually-grounded dialogue, the discourse processing capabilities of these models need to be augmented. To address this issue, we propose fine-tuning a causal large language model (LLM) to generate definite descriptions that summarize coreferential information found in the linguistic context of references. We then use a pretrained VLM to identify referents based on the generated descriptions, zero-shot. We evaluate our approach on a manually annotated dataset of visually-grounded dialogues and achieve results that, on average, exceed the performance of the baselines we compare against. Furthermore, we find that using referent descriptions based on larger context windows has the potential to yield higher returns.</abstract>
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%0 Conference Proceedings
%T Resolving References in Visually-Grounded Dialogue via Text Generation
%A Willemsen, Bram
%A Qian, Livia
%A Skantze, Gabriel
%Y Stoyanchev, Svetlana
%Y Joty, Shafiq
%Y Schlangen, David
%Y Dusek, Ondrej
%Y Kennington, Casey
%Y Alikhani, Malihe
%S Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F willemsen-etal-2023-resolving
%X Vision-language models (VLMs) have shown to be effective at image retrieval based on simple text queries, but text-image retrieval based on conversational input remains a challenge. Consequently, if we want to use VLMs for reference resolution in visually-grounded dialogue, the discourse processing capabilities of these models need to be augmented. To address this issue, we propose fine-tuning a causal large language model (LLM) to generate definite descriptions that summarize coreferential information found in the linguistic context of references. We then use a pretrained VLM to identify referents based on the generated descriptions, zero-shot. We evaluate our approach on a manually annotated dataset of visually-grounded dialogues and achieve results that, on average, exceed the performance of the baselines we compare against. Furthermore, we find that using referent descriptions based on larger context windows has the potential to yield higher returns.
%R 10.18653/v1/2023.sigdial-1.43
%U https://aclanthology.org/2023.sigdial-1.43
%U https://doi.org/10.18653/v1/2023.sigdial-1.43
%P 457-469

Markdown (Informal)

[Resolving References in Visually-Grounded Dialogue via Text Generation](https://aclanthology.org/2023.sigdial-1.43) (Willemsen et al., SIGDIAL 2023)

• Resolving References in Visually-Grounded Dialogue via Text Generation (Willemsen et al., SIGDIAL 2023)

ACL

• Bram Willemsen, Livia Qian, and Gabriel Skantze. 2023. Resolving References in Visually-Grounded Dialogue via Text Generation. In Proceedings of the 24th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 457–469, Prague, Czechia. Association for Computational Linguistics.