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Found 7 papers, 4 papers with code
GCDF1: A Goal- and Context- Driven F-Score for Evaluating User Models
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.
uFACT: Unfaithful Alien-Corpora Training for Semantically Consistent Data-to-Text Generation
Our approach is to augment the training set of a given target corpus with alien corpora which have different semantic representations.
LUCID: LLM-Generated Utterances for Complex and Interesting Dialogues
Moreover, creating high quality dialogue data has until now required considerable human input, limiting both the scale of these datasets and the ability to rapidly bootstrap data for a new target domain.
Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries
To address this problem, we handle the task of conversation retrieval based on text summaries of the conversations.
Fine-grained Late-interaction Multi-modal Retrieval for Retrieval Augmented Visual Question Answering
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
Grounding Description-Driven Dialogue State Trackers with Knowledge-Seeking Turns
Schema-guided dialogue state trackers can generalise to new domains without further training, yet they are sensitive to the writing style of the schemata.
Monitoring and explainability of models in production
The machine learning lifecycle extends beyond the deployment stage.
