A Split-and-Recombine Approach for Follow-up Query Analysis

Liu, Qian; Chen, Bei; Liu, Haoyan; Lou, Jian–Guang; Fang, Lei; Zhou, Bin; Zhang, Dongmei

doi:10.18653/v1/d19-1535

Cited by 6 publications

(3 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Each cell belongs to one of three edit types: None, Substitute and Insert. Liu et al (2019a) imposed an intermediate structure span and decomposed the incomplete utterance rewriting into two sub-tasks. In dialogue generation, Pan et al (2019) presented a cascaded model which first picks words from the context via BERT, and then combines these words to generate the rewritten utterance, and Su et al (2019) distinguished the weights of context utterances and the incomplete utterance using a hyper-parameter λ.…”

Section: Related Workmentioning

confidence: 99%

Incomplete Utterance Rewriting as Semantic Segmentation

Liu¹,

Chen²,

Lou³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Self Cite

View full text Add to dashboard Cite

Recent years the task of incomplete utterance rewriting has raised a large attention. Previous works usually shape it as a machine translation task and employ sequence to sequence based architecture with copy mechanism. In this paper, we present a novel and extensive approach, which formulates it as a semantic segmentation task. Instead of generating from scratch, such a formulation introduces edit operations and shapes the problem as prediction of a word-level edit matrix. Benefiting from being able to capture both local and global information, our approach achieves state-ofthe-art performance on several public datasets. Furthermore, our approach is four times faster than the standard approach in inference.

show abstract

Section: Related Workmentioning

confidence: 99%

Incomplete Utterance Rewriting as Semantic Segmentation

Liu¹,

Chen²,

Lou³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Self Cite

View full text Add to dashboard Cite

show abstract

“…In addition, the applications of RL on language involves topics such as natural language generation [11], conversational semantic parsing [23] and text classification [42].…”

Section: Related Workmentioning

confidence: 99%

Compositional Generalization by Learning Analytical Expressions

Liu¹,

An²,

Lou³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Compositional generalization is a basic but essential intellective capability of human beings, which allows us to recombine known parts readily. However, existing neural network based models have been proven to be extremely deficient in such a capability. Inspired by work in cognition which argues compositionality can be captured by variable slots with symbolic functions, we present a refreshing view that connects a memory-augmented neural model with analytical expressions, to achieve compositional generalization. Our model consists of two cooperative neural modules Composer and Solver, fitting well with the cognitive argument while still being trained in an end-to-end manner via a hierarchical reinforcement learning algorithm. Experiments on a well-known benchmark SCAN demonstrate that our model seizes a great ability of compositional generalization, solving all challenges addressed by previous works with 100% accuracies.

show abstract

“…Moreover, the chance of a search failing due to looking up irrelevant keywords in the knowledge base becomes lower. Query splitting techniques are therefore relevant in searching [11].…”

Section: Introductionmentioning

confidence: 99%

Handling Complex Queries Using Query Trees

Vemuru¹,

John²,

Rao³

2021

Preprint

View full text Add to dashboard Cite

Humans can easily parse and find answers to complex queries such as "What was the capital of the country of the discoverer of the element which has atomic number 1?" by breaking them up into small pieces, querying these appropriately, and assembling a final answer. However, contemporary search engines lack such capability and fail to handle even slightly complex queries. Search engines process queries by identifying keywords and searching against them in knowledge bases or indexed web pages. The results are, therefore, dependent on the keywords and how well the search engine handles them. In our work, we propose a three-step approach called parsing, tree generation, and querying (PTGQ) for effective searching of larger and more expressive queries of potentially unbounded complexity. PTGQ parses a complex query and constructs a query tree where each node represents a simple query. It then processes the complex query by recursively querying a back-end search engine, going over the corresponding query tree in postorder. Using PTGQ makes sure that the search engine always handles a simpler query containing very few keywords. Results demonstrate that PTGQ can handle queries of much higher complexity than standalone search engines.

show abstract

A Split-and-Recombine Approach for Follow-up Query Analysis

Cited by 6 publications

References 30 publications

Incomplete Utterance Rewriting as Semantic Segmentation

Incomplete Utterance Rewriting as Semantic Segmentation

Compositional Generalization by Learning Analytical Expressions

Handling Complex Queries Using Query Trees

Contact Info

Product

Resources

About