SARG: A Novel Semi Autoregressive Generator for Multi-turn Incomplete Utterance Restoration

Dialogue systems in open domain have achieved great success due to the easily obtained single-turn corpus and the development of deep learning, but the multi-turn scenario is still a challenge because of the frequent coreference and information omission. In this paper, we investigate the incomplete utterance restoration which has brought general improvement over multi-turn dialogue systems in recent studies. Meanwhile, jointly inspired by the autoregression for text generation and the sequence labeling for text editing, we propose a novel semi autoregressive generator (SARG) with the high efficiency and flexibility. Moreover, experiments on two benchmarks show that our proposed model significantly outperforms the state-of-the-art models in terms of quality and inference speed.Comment: Accepted to AAAI 202

Incomplete Utterance Rewriting as Sequential Greedy Tagging

The task of incomplete utterance rewriting has recently gotten much attention. Previous models struggled to extract information from the dialogue context, as evidenced by the low restoration scores. To address this issue, we propose a novel sequence tagging-based model, which is more adept at extracting information from context. Meanwhile, we introduce speaker-aware embedding to model speaker variation. Experiments on multiple public datasets show that our model achieves optimal results on all nine restoration scores while having other metric scores comparable to previous state-of-the-art models. Furthermore, benefitting from the model's simplicity, our approach outperforms most previous models on inference speed.Comment: arXiv admin note: text overlap with arXiv:2009.13166 by other author

Enhance Incomplete Utterance Restoration by Joint Learning Token Extraction and Text Generation

This paper introduces a model for incomplete utterance restoration (IUR). Different from prior studies that only work on extraction or abstraction datasets, we design a simple but effective model, working for both scenarios of IUR. Our design simulates the nature of IUR, where omitted tokens from the context contribute to restoration. From this, we construct a Picker that identifies the omitted tokens. To support the picker, we design two label creation methods (soft and hard labels), which can work in cases of no annotation of the omitted tokens. The restoration is done by using a Generator with the help of the Picker on joint learning. Promising results on four benchmark datasets in extraction and abstraction scenarios show that our model is better than the pretrained T5 and non-generative language model methods in both rich and limited training data settings. The code will be also available.Comment: This is the early version of the paper accepted by NAACL 2022. It includes 10 pages, 2 figure

Multi-Granularity Information Interaction Framework for Incomplete Utterance Rewriting

Recent approaches in Incomplete Utterance Rewriting (IUR) fail to capture the source of important words, which is crucial to edit the incomplete utterance, and introduce words from irrelevant utterances. We propose a novel and effective multi-task information interaction framework including context selection, edit matrix construction, and relevance merging to capture the multi-granularity of semantic information. Benefiting from fetching the relevant utterance and figuring out the important words, our approach outperforms existing state-of-the-art models on two benchmark datasets Restoration-200K and CANAND in this field. Code will be provided on \url{https://github.com/yanmenxue/QR}.Comment: Findings of EMNLP2023 (short

Grounding semantics in robots for Visual Question Answering

In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning

Language technologies in speech-enabled second language learning games : from reading to dialogue

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 237-244).Second language learning has become an important societal need over the past decades. Given that the number of language teachers is far below demand, computer-aided language learning software is becoming a promising supplement to traditional classroom learning, as well as potentially enabling new opportunities for self-learning. The use of speech technologies is especially attractive to offer students unlimited chances for speaking exercises. To create helpful and intelligent speaking exercises on a computer, it is necessary for the computer to not only recognize the acoustics, but also to understand the meaning and give appropriate responses. Nevertheless, most existing speech-enabled language learning software focuses only on speech recognition and pronunciation training. Very few have emphasized exercising the student's composition and comprehension abilities and adopting language technologies to enable free-form conversation emulating a real human tutor. This thesis investigates the critical functionalities of a computer-aided language learning system, and presents a generic framework as well as various language- and domain-independent modules to enable building complex speech-based language learning systems. Four games have been designed and implemented using the framework and the modules to demonstrate their usability and flexibility, where dynamic content creation, automatic assessment, and automatic assistance are emphasized. The four games, reading, translation, question-answering and dialogue, offer different activities with gradually increasing difficulty, and involve a wide range of language processing techniques, such as language understanding, language generation, question generation, context resolution, dialogue management and user simulation. User studies with real subjects show that the systems were well received and judged to be helpful.by Yushi Xu.Ph.D