1 code implementation • EANCS 2021 • Alexandru Coca, Bo-Hsiang Tseng, Bill Byrne
The evaluation of dialogue systems in interaction with simulated users has been proposed to improve turn-level, corpus-based metrics which can only evaluate test cases encountered in a corpus and cannot measure system’s ability to sustain multi-turn interactions.
no code implementations • Findings (ACL) 2022 • Tisha Anders, Alexandru Coca, Bill Byrne
Our approach is to augment the training set of a given target corpus with alien corpora which have different semantic representations.
1 code implementation • 1 Mar 2024 • Joe Stacey, Jianpeng Cheng, John Torr, Tristan Guigue, Joris Driesen, Alexandru Coca, Mark Gaynor, Anders Johannsen
Spurred by recent advances in Large Language Models (LLMs), virtual assistants are poised to take a leap forward in terms of their dialogue capabilities.
no code implementations • 20 Feb 2024 • Seanie Lee, Jianpeng Cheng, Joris Driesen, Alexandru Coca, Anders Johannsen
To address this problem, we handle the task of conversation retrieval based on text summaries of the conversations.
1 code implementation • NeurIPS 2023 • Weizhe Lin, Jinghong Chen, Jingbiao Mei, Alexandru Coca, Bill Byrne
FLMR addresses two major limitations in RA-VQA's retriever: (1) the image representations obtained via image-to-text transforms can be incomplete and inaccurate and (2) relevance scores between queries and documents are computed with one-dimensional embeddings, which can be insensitive to finer-grained relevance.
Ranked #1 on Retrieval on OK-VQA
no code implementations • 23 Sep 2023 • Alexandru Coca, Bo-Hsiang Tseng, Jinghong Chen, Weizhe Lin, Weixuan Zhang, Tisha Anders, Bill Byrne
Schema-guided dialogue state trackers can generalise to new domains without further training, yet they are sensitive to the writing style of the schemata.
1 code implementation • 13 Jul 2020 • Janis Klaise, Arnaud Van Looveren, Clive Cox, Giovanni Vacanti, Alexandru Coca
The machine learning lifecycle extends beyond the deployment stage.