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Found 6 papers, 1 papers with code
CO3: Low-resource Contrastive Co-training for Generative Conversational Query Rewrite
To this end, we study low-resource generative conversational query rewrite that is robust to both noise and language style shift.
How Can Large Language Models Understand Spatial-Temporal Data?
We tackle the data mismatch by proposing: 1) STG-Tokenizer: This spatial-temporal graph tokenizer transforms intricate graph data into concise tokens capturing both spatial and temporal relationships; 2) STG-Adapter: This minimalistic adapter, consisting of linear encoding and decoding layers, bridges the gap between tokenized data and LLM comprehension.
McQueen: a Benchmark for Multimodal Conversational Query Rewrite
In this paper, we propose the task of multimodal conversational query rewrite (McQR), which performs query rewrite under the multimodal visual conversation setting.
Can You be More Social? Injecting Politeness and Positivity into Task-Oriented Conversational Agents
The second component of the research is the construction of a conversational agent model capable of injecting social language into an agent's responses while still preserving content.
Joint Intent Detection and Entity Linking on Spatial Domain Queries
Continuous efforts have been devoted to language understanding (LU) for conversational queries with the fast and wide-spread popularity of voice assistants.
Joint Contextual Modeling for ASR Correction and Language Understanding
As a baseline approach, we trained task-specific Statistical Language Models (SLM) and fine-tuned state-of-the-art Generalized Pre-training (GPT) Language Model to re-rank the n-best ASR hypotheses, followed by a model to identify the dialog act and slots.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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