Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce

Abstract

Building multi-turn information-seeking conversation systems is an important and challenging research topic. Although several advanced neural text matching models have been proposed for this task, they are generally not efficient for industrial applications. Furthermore, they rely on a large amount of labeled data, which may not be available in real-world applications. To alleviate these problems, we study transfer learning for multi-turn information seeking conversations in this paper. We first propose an efficient and effective multiturn conversation model based on convolutional neural networks. After that, we extend our model to adapt the knowledge learned from a resource-rich domain to enhance the performance. Finally, we deployed our model in an industrial chatbot called AliMe Assist 1 and observed a significant improvement over the existing online model. 2 According to a statistic in AliMe Assist in Alibaba Group

Reversing Gradients in Adversarial Domain Adaptation for Question Deduplication and Textual Entailment Tasks

“…T1, T2 and T3 outperform baseline, hence enforcing the effectiveness of adversarial domain adaptation in all tasks in Table 1. T3 outperforms T2, thus indicating that learning a combination of domain specific and shared representations is quite beneficial for all domain transfer experiments in Table 1. This observation was also noted by Qiu et al (Qiu et al, 2018), even if without the use of gradient reversal.…”

“…To address this issue, Qiu et al used both shared domain invariant and domain specific features: while the shared features are learned by maximizing domain discriminator loss, the domain specific features are learned by jointly minimizing the task loss and the domain classification loss by domain specific discriminators (Qiu et al, 2018). Similar ideas were put forth by Peng et al for cross-domain sentiment classification where they demonstrate the effectiveness of using both domain specific and domain invariant features (Peng et al, 2018).…”

Quick and (not so) Dirty: Unsupervised Selection of Justification Sentences for Multi-hop Question Answering

“…Note that some of the works discussed here transfer knowledge from external datasets into the QA task they address (Chung et al, 2017;Pan et al, 2019;Min et al, 2017;Qiu et al, 2018;Chen et al, 2017). In this work, we focus solely on the resources provided in the task itself because such compatible external resources may not be available in real-world applications of QA.…”

Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce

“…Following Qiu et al [4], we evaluate the model performance on five automatic evaluation metrics: MRR, R 10 @1, R 10 @2, R 10 @5, and R 2 @1. R n @k calculates the recall of the true positive predefined questions among the k selected candidates from n available candidates.…”