29 research outputs found
Target-Guided Open-Domain Conversation Planning
Prior studies addressing target-oriented conversational tasks lack a crucial
notion that has been intensively studied in the context of goal-oriented
artificial intelligence agents, namely, planning. In this study, we propose the
task of Target-Guided Open-Domain Conversation Planning (TGCP) task to evaluate
whether neural conversational agents have goal-oriented conversation planning
abilities. Using the TGCP task, we investigate the conversation planning
abilities of existing retrieval models and recent strong generative models. The
experimental results reveal the challenges facing current technology.Comment: 9 pages, Accepted to The 29th International Conference on
Computational Linguistics (COLING 2022
Dynamic Knowledge Routing Network For Target-Guided Open-Domain Conversation
Target-guided open-domain conversation aims to proactively and naturally
guide a dialogue agent or human to achieve specific goals, topics or keywords
during open-ended conversations. Existing methods mainly rely on single-turn
datadriven learning and simple target-guided strategy without considering
semantic or factual knowledge relations among candidate topics/keywords. This
results in poor transition smoothness and low success rate. In this work, we
adopt a structured approach that controls the intended content of system
responses by introducing coarse-grained keywords, attains smooth conversation
transition through turn-level supervised learning and knowledge relations
between candidate keywords, and drives an conversation towards an specified
target with discourse-level guiding strategy. Specially, we propose a novel
dynamic knowledge routing network (DKRN) which considers semantic knowledge
relations among candidate keywords for accurate next topic prediction of next
discourse. With the help of more accurate keyword prediction, our
keyword-augmented response retrieval module can achieve better retrieval
performance and more meaningful conversations. Besides, we also propose a novel
dual discourse-level target-guided strategy to guide conversations to reach
their goals smoothly with higher success rate. Furthermore, to push the
research boundary of target-guided open-domain conversation to match real-world
scenarios better, we introduce a new large-scale Chinese target-guided
open-domain conversation dataset (more than 900K conversations) crawled from
Sina Weibo. Quantitative and human evaluations show our method can produce
meaningful and effective target-guided conversations, significantly improving
over other state-of-the-art methods by more than 20% in success rate and more
than 0.6 in average smoothness score.Comment: 8 pages, 2 figues, 6tables, AAAI2020, fix our model's abbreviatio
Data Augmentation for Conversational AI
Advancements in conversational systems have revolutionized information
access, surpassing the limitations of single queries. However, developing
dialogue systems requires a large amount of training data, which is a challenge
in low-resource domains and languages. Traditional data collection methods like
crowd-sourcing are labor-intensive and time-consuming, making them ineffective
in this context. Data augmentation (DA) is an affective approach to alleviate
the data scarcity problem in conversational systems. This tutorial provides a
comprehensive and up-to-date overview of DA approaches in the context of
conversational systems. It highlights recent advances in conversation
augmentation, open domain and task-oriented conversation generation, and
different paradigms of evaluating these models. We also discuss current
challenges and future directions in order to help researchers and practitioners
to further advance the field in this area
Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog
Many efforts have been made to construct dialog systems for different types
of conversations, such as task-oriented dialog (TOD) and open-domain dialog
(ODD). To better mimic human-level conversations that usually fuse various
dialog modes, it is essential to build a system that can effectively handle
both TOD and ODD and access different knowledge sources. To address the lack of
available data for the fused task, we propose a framework for automatically
generating dialogues that combine knowledge-grounded ODDs and TODs in various
settings. Additionally, we introduce a unified model PivotBot that is capable
of appropriately adopting TOD and ODD modes and accessing different knowledge
sources in order to effectively tackle the fused task. Evaluation results
demonstrate the superior ability of the proposed model to switch seamlessly
between TOD and ODD tasks.Comment: SIGDIAL 202
A Controllable Model of Grounded Response Generation
Current end-to-end neural conversation models inherently lack the flexibility
to impose semantic control in the response generation process, often resulting
in uninteresting responses. Attempts to boost informativeness alone come at the
expense of factual accuracy, as attested by pretrained language models'
propensity to "hallucinate" facts. While this may be mitigated by access to
background knowledge, there is scant guarantee of relevance and informativeness
in generated responses. We propose a framework that we call controllable
grounded response generation (CGRG), in which lexical control phrases are
either provided by a user or automatically extracted by a control phrase
predictor from dialogue context and grounding knowledge. Quantitative and
qualitative results show that, using this framework, a transformer based model
with a novel inductive attention mechanism, trained on a conversation-like
Reddit dataset, outperforms strong generation baselines.Comment: AAAI 202
Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation
Target-oriented dialogue systems, designed to proactively steer conversations
toward predefined targets or accomplish specific system-side goals, are an
exciting area in conversational AI. In this work, by formulating a <dialogue
act, topic> pair as the conversation target, we explore a novel problem of
personalized target-oriented dialogue by considering personalization during the
target accomplishment process. However, there remains an emergent need for
high-quality datasets, and building one from scratch requires tremendous human
effort. To address this, we propose an automatic dataset curation framework
using a role-playing approach. Based on this framework, we construct a
large-scale personalized target-oriented dialogue dataset, TopDial, which
comprises about 18K multi-turn dialogues. The experimental results show that
this dataset is of high quality and could contribute to exploring personalized
target-oriented dialogue.Comment: Accepted to EMNLP-2023 main conferenc
Keyword-Guided Neural Conversational Model
We study the problem of imposing conversational goals/keywords on open-domain
conversational agents, where the agent is required to lead the conversation to
a target keyword smoothly and fast. Solving this problem enables the
application of conversational agents in many real-world scenarios, e.g.,
recommendation and psychotherapy. The dominant paradigm for tackling this
problem is to 1) train a next-turn keyword classifier, and 2) train a
keyword-augmented response retrieval model. However, existing approaches in
this paradigm have two limitations: 1) the training and evaluation datasets for
next-turn keyword classification are directly extracted from conversations
without human annotations, thus, they are noisy and have low correlation with
human judgements, and 2) during keyword transition, the agents solely rely on
the similarities between word embeddings to move closer to the target keyword,
which may not reflect how humans converse. In this paper, we assume that human
conversations are grounded on commonsense and propose a keyword-guided neural
conversational model that can leverage external commonsense knowledge graphs
(CKG) for both keyword transition and response retrieval. Automatic evaluations
suggest that commonsense improves the performance of both next-turn keyword
prediction and keyword-augmented response retrieval. In addition, both
self-play and human evaluations show that our model produces responses with
smoother keyword transition and reaches the target keyword faster than
competitive baselines.Comment: AAAI-202
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