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Found 11 papers, 3 papers with code
KILM: Knowledge Injection into Encoder-Decoder Language Models
Large pre-trained language models (PLMs) have been shown to retain implicit knowledge within their parameters.
Using In-Context Learning to Improve Dialogue Safety
For instance, using automatic evaluation, we find our best fine-tuned baseline only generates safe responses to unsafe dialogue contexts from DiaSafety 4. 04% more than our approach.
Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems
As more users across the world are interacting with dialog agents in their daily life, there is a need for better speech understanding that calls for renewed attention to the dynamics between research in automatic speech recognition (ASR) and natural language understanding (NLU).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Zero-Shot Controlled Generation with Encoder-Decoder Transformers
In this work, we propose novel approaches for controlling encoder-decoder transformer-based NLG models in zero-shot.
VISITRON: Visual Semantics-Aligned Interactively Trained Object-Navigator
Interactive robots navigating photo-realistic environments need to be trained to effectively leverage and handle the dynamic nature of dialogue in addition to the challenges underlying vision-and-language navigation (VLN).
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Interactive evaluation of dialog, and 4.
Language Model is All You Need: Natural Language Understanding as Question Answering
Different flavors of transfer learning have shown tremendous impact in advancing research and applications of machine learning.
Learning Question-Guided Video Representation for Multi-Turn Video Question Answering
Understanding and conversing about dynamic scenes is one of the key capabilities of AI agents that navigate the environment and convey useful information to humans.
Towards Universal Dialogue Act Tagging for Task-Oriented Dialogues
In unsupervised learning experiments we achieve an F1 score of 54. 1% on system turns in human-human dialogues.
HyST: A Hybrid Approach for Flexible and Accurate Dialogue State Tracking
In this work, we analyze the performance of these two alternative dialogue state tracking methods, and present a hybrid approach (HyST) which learns the appropriate method for each slot type.
Ranked #18 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.0
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Building a Conversational Agent Overnight with Dialogue Self-Play
We propose Machines Talking To Machines (M2M), a framework combining automation and crowdsourcing to rapidly bootstrap end-to-end dialogue agents for goal-oriented dialogues in arbitrary domains.
