30 research outputs found
Autoregressive Entity Generation for End-to-End Task-Oriented Dialog
Task-oriented dialog (TOD) systems often require interaction with an external
knowledge base to retrieve necessary entity (e.g., restaurant) information to
support the response generation. Most current end-to-end TOD systems either
retrieve the KB information explicitly or embed it into model parameters for
implicit access.~While the former approach demands scanning the KB at each turn
of response generation, which is inefficient when the KB scales up, the latter
approach shows higher flexibility and efficiency. In either approach, the
systems may generate a response with conflicting entity information. To address
this issue, we propose to generate the entity autoregressively first and
leverage it to guide the response generation in an end-to-end system. To ensure
entity consistency, we impose a trie constraint on entity generation. We also
introduce a logit concatenation strategy to facilitate gradient backpropagation
for end-to-end training. Experiments on MultiWOZ 2.1 single and CAMREST show
that our system can generate more high-quality and entity-consistent responses.Comment: Accepted to COLING 202
Adaptive Natural Language Generation for Task-oriented Dialogue via Reinforcement Learning
When a natural language generation (NLG) component is implemented in a
real-world task-oriented dialogue system, it is necessary to generate not only
natural utterances as learned on training data but also utterances adapted to
the dialogue environment (e.g., noise from environmental sounds) and the user
(e.g., users with low levels of understanding ability). Inspired by recent
advances in reinforcement learning (RL) for language generation tasks, we
propose ANTOR, a method for Adaptive Natural language generation for
Task-Oriented dialogue via Reinforcement learning. In ANTOR, a natural language
understanding (NLU) module, which corresponds to the user's understanding of
system utterances, is incorporated into the objective function of RL. If the
NLG's intentions are correctly conveyed to the NLU, which understands a
system's utterances, the NLG is given a positive reward. We conducted
experiments on the MultiWOZ dataset, and we confirmed that ANTOR could generate
adaptive utterances against speech recognition errors and the different
vocabulary levels of users.Comment: Accepted by COLING 202
Contextual Understanding in Neural Dialog Systems: the Integration of External Knowledge Graphs for Generating Coherent and Knowledge-rich Conversations
The integration of external knowledge graphs has emerged as a powerful approach to enrich conversational AI systems with coherent and knowledge-rich conversations. This paper provides an overview of the integration process and highlights its benefits. Knowledge graphs serve as structured representations of information, capturing the relationships between entities through nodes and edges. They offer an organized and efficient means of representing factual knowledge. External knowledge graphs, such as DBpedia, Wikidata, Freebase, and Google's Knowledge Graph, are pre-existing repositories that encompass a wide range of information across various domains. These knowledge graphs are compiled by aggregating data from diverse sources, including online encyclopedias, databases, and structured repositories. To integrate an external knowledge graph into a conversational AI system, a connection needs to be established between the system and the knowledge graph. This can be achieved through APIs or by importing a copy of the knowledge graph into the AI system's internal storage. Once integrated, the conversational AI system can query the knowledge graph to retrieve relevant information when a user poses a question or makes a statement. When analyzing user inputs, the conversational AI system identifies entities or concepts that require additional knowledge. It then formulates queries to retrieve relevant information from the integrated knowledge graph. These queries may involve searching for specific entities, retrieving related entities, or accessing properties and attributes associated with the entities. The obtained information is used to generate coherent and knowledge-rich responses. By integrating external knowledge graphs, conversational AI systems can augment their internal knowledge base and provide more accurate and up-to-date responses. The retrieved information allows the system to extract relevant facts, provide detailed explanations, or offer additional context. This integration empowers AI systems to deliver comprehensive and insightful responses that enhance user experience. As external knowledge graphs are regularly updated with new information and improvements, conversational AI systems should ensure their integrated knowledge graphs remain current. This can be achieved through periodic updates, either by synchronizing the system's internal representation with the external knowledge graph or by querying the external knowledge graph in real-time
Causal-aware Safe Policy Improvement for Task-oriented dialogue
The recent success of reinforcement learning's (RL) in solving complex tasks
is most often attributed to its capacity to explore and exploit an environment
where it has been trained. Sample efficiency is usually not an issue since
cheap simulators are available to sample data on-policy. On the other hand,
task oriented dialogues are usually learnt from offline data collected using
human demonstrations. Collecting diverse demonstrations and annotating them is
expensive. Unfortunately, use of RL methods trained on off-policy data are
prone to issues of bias and generalization, which are further exacerbated by
stochasticity in human response and non-markovian belief state of a dialogue
management system. To this end, we propose a batch RL framework for task
oriented dialogue policy learning: causal aware safe policy improvement
(CASPI). This method gives guarantees on dialogue policy's performance and also
learns to shape rewards according to intentions behind human responses, rather
than just mimicking demonstration data; this couple with batch-RL helps overall
with sample efficiency of the framework. We demonstrate the effectiveness of
this framework on a dialogue-context-to-text Generation and end-to-end dialogue
task of the Multiwoz2.0 dataset. The proposed method outperforms the current
state of the art on these metrics, in both case. In the end-to-end case, our
method trained only on 10\% of the data was able to out perform current state
in three out of four evaluation metrics