ﻻ يوجد ملخص باللغة العربية
The traditional dialogue state tracking (DST) task tracks the dialogue state given the past history of user and agent utterances. This paper proposes to replace the utterances before the current turn with a formal representation, which is used as the context in a semantic parser mapping the current user utterance to its formal meaning. In addition, we propose TOC (Task-Oriented Context), a formal dialogue state representation. This approach eliminates the need to parse a long history of natural language utterances; however, it adds complexity to the dialogue annotations. We propose Skim, a contextual semantic parser, trained with a sample-efficient training strategy: (1) a novel abstract dialogue state machine to synthesize training sets with TOC annotations; (2) data augmentation with automatic paraphrasing, (3) few-shot training, and (4) self-training. This paper also presents MultiWOZ 2.4, which consists of the full test set and a partial validation set of MultiWOZ 2.1, reannotated with the TOC representation. Skim achieves 78% turn-by-turn exact match accuracy and 85% slot accuracy, while our annotation effort amounts to only 2% of the training data used in MultiWOZ 2.1. The MultiWOZ 2.4 dataset will be released upon publication.
We explore the use of large pretrained language models as few-shot semantic parsers. The goal in semantic parsing is to generate a structured meaning representation given a natural language input. However, language models are trained to generate natu
Dialogue State Tracking (DST) forms a core component of automated chatbot based systems designed for specific goals like hotel, taxi reservation, tourist information, etc. With the increasing need to deploy such systems in new domains, solving the pr
In this work, we investigate the problems of semantic parsing in a few-shot learning setting. In this setting, we are provided with utterance-logical form pairs per new predicate. The state-of-the-art neural semantic parsers achieve less than 25% acc
Dense retrieval (DR) has the potential to resolve the query understanding challenge in conversational search by matching in the learned embedding space. However, this adaptation is challenging due to DR models extra needs for supervision signals and
The structured representation for semantic parsing in task-oriented assistant systems is geared towards simple understanding of one-turn queries. Due to the limitations of the representation, the session-based properties such as co-reference resoluti