2 research outputs found
Towards a Neural Era in Dialogue Management for Collaboration: A Literature Survey
Dialogue-based human-AI collaboration can revolutionize collaborative
problem-solving, creative exploration, and social support. To realize this
goal, the development of automated agents proficient in skills such as
negotiating, following instructions, establishing common ground, and
progressing shared tasks is essential. This survey begins by reviewing the
evolution of dialogue management paradigms in collaborative dialogue systems,
from traditional handcrafted and information-state based methods to AI
planning-inspired approaches. It then shifts focus to contemporary data-driven
dialogue management techniques, which seek to transfer deep learning successes
from form-filling and open-domain settings to collaborative contexts. The paper
proceeds to analyze a selected set of recent works that apply neural approaches
to collaborative dialogue management, spotlighting prevailing trends in the
field. This survey hopes to provide foundational background for future
advancements in collaborative dialogue management, particularly as the dialogue
systems community continues to embrace the potential of large language models
Agreement Tracking for Multi-Issue Negotiation Dialogues
Automated negotiation support systems aim to help human negotiators reach
more favorable outcomes in multi-issue negotiations (e.g., an employer and a
candidate negotiating over issues such as salary, hours, and promotions before
a job offer). To be successful, these systems must accurately track agreements
reached by participants in real-time. Existing approaches either focus on
task-oriented dialogues or produce unstructured outputs, rendering them
unsuitable for this objective. Our work introduces the novel task of agreement
tracking for two-party multi-issue negotiations, which requires continuous
monitoring of agreements within a structured state space. To address the
scarcity of annotated corpora with realistic multi-issue negotiation dialogues,
we use GPT-3 to build GPT-Negochat, a synthesized dataset that we make publicly
available. We present a strong initial baseline for our task by
transfer-learning a T5 model trained on the MultiWOZ 2.4 corpus. Pre-training
T5-small and T5-base on MultiWOZ 2.4's DST task enhances results by 21% and 9%
respectively over training solely on GPT-Negochat. We validate our method's
sample-efficiency via smaller training subset experiments. By releasing
GPT-Negochat and our baseline models, we aim to encourage further research in
multi-issue negotiation dialogue agreement tracking