2,313 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
INA: An Integrative Approach for Enhancing Negotiation Strategies with Reward-Based Dialogue System
In this paper, we propose a novel negotiation dialogue agent designed for the
online marketplace. Our agent is integrative in nature i.e, it possesses the
capability to negotiate on price as well as other factors, such as the addition
or removal of items from a deal bundle, thereby offering a more flexible and
comprehensive negotiation experience. We create a new dataset called
Integrative Negotiation Dataset (IND) to enable this functionality. For this
dataset creation, we introduce a new semi-automated data creation method, which
combines defining negotiation intents, actions, and intent-action simulation
between users and the agent to generate potential dialogue flows. Finally, the
prompting of GPT-J, a state-of-the-art language model, is done to generate
dialogues for a given intent, with a human-in-the-loop process for post-editing
and refining minor errors to ensure high data quality. We employ a set of novel
rewards, specifically tailored for the negotiation task to train our
Negotiation Agent, termed as the Integrative Negotiation Agent (INA). These
rewards incentivize the chatbot to learn effective negotiation strategies that
can adapt to various contextual requirements and price proposals. By leveraging
the IND, we train our model and conduct experiments to evaluate the
effectiveness of our reward-based dialogue system for negotiation. Our results
demonstrate that the proposed approach and reward system significantly enhance
the agent's negotiation capabilities. The INA successfully engages in
integrative negotiations, displaying the ability to dynamically adjust prices
and negotiate the inclusion or exclusion of items in a bundle dea
Prompting and Evaluating Large Language Models for Proactive Dialogues: Clarification, Target-guided, and Non-collaboration
Conversational systems based on Large Language Models (LLMs), such as
ChatGPT, show exceptional proficiency in context understanding and response
generation. However, despite their impressive capabilities, they still possess
limitations, such as providing randomly-guessed answers to ambiguous queries or
failing to refuse users' requests, both of which are considered aspects of a
conversational agent's proactivity. This raises the question of whether
LLM-based conversational systems are equipped to handle proactive dialogue
problems. In this work, we conduct a comprehensive analysis of LLM-based
conversational systems, specifically focusing on three aspects of proactive
dialogue systems: clarification, target-guided, and non-collaborative
dialogues. To trigger the proactivity of LLMs, we propose the Proactive
Chain-of-Thought prompting scheme, which augments LLMs with the goal planning
capability over descriptive reasoning chains. Empirical findings are discussed
to promote future studies on LLM-based proactive dialogue systems.Comment: Work in progres
Adversarial Language Games for Advanced Natural Language Intelligence
We study the problem of adversarial language games, in which multiple agents
with conflicting goals compete with each other via natural language
interactions. While adversarial language games are ubiquitous in human
activities, little attention has been devoted to this field in natural language
processing. In this work, we propose a challenging adversarial language game
called Adversarial Taboo as an example, in which an attacker and a defender
compete around a target word. The attacker is tasked with inducing the defender
to utter the target word invisible to the defender, while the defender is
tasked with detecting the target word before being induced by the attacker. In
Adversarial Taboo, a successful attacker must hide its intention and subtly
induce the defender, while a competitive defender must be cautious with its
utterances and infer the intention of the attacker. Such language abilities can
facilitate many important downstream NLP tasks. To instantiate the game, we
create a game environment and a competition platform. Comprehensive experiments
and empirical studies on several baseline attack and defense strategies show
promising and interesting results. Based on the analysis on the game and
experiments, we discuss multiple promising directions for future research.Comment: Accepted by AAAI 202
Recommendation as a Communication Game: Self-Supervised Bot-Play for Goal-oriented Dialogue
Traditional recommendation systems produce static rather than interactive
recommendations invariant to a user's specific requests, clarifications, or
current mood, and can suffer from the cold-start problem if their tastes are
unknown. These issues can be alleviated by treating recommendation as an
interactive dialogue task instead, where an expert recommender can sequentially
ask about someone's preferences, react to their requests, and recommend more
appropriate items. In this work, we collect a goal-driven recommendation
dialogue dataset (GoRecDial), which consists of 9,125 dialogue games and 81,260
conversation turns between pairs of human workers recommending movies to each
other. The task is specifically designed as a cooperative game between two
players working towards a quantifiable common goal. We leverage the dataset to
develop an end-to-end dialogue system that can simultaneously converse and
recommend. Models are first trained to imitate the behavior of human players
without considering the task goal itself (supervised training). We then
finetune our models on simulated bot-bot conversations between two paired
pre-trained models (bot-play), in order to achieve the dialogue goal. Our
experiments show that models finetuned with bot-play learn improved dialogue
strategies, reach the dialogue goal more often when paired with a human, and
are rated as more consistent by humans compared to models trained without
bot-play. The dataset and code are publicly available through the ParlAI
framework.Comment: EMNLP 201
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