Modelling domain relationships for transfer learning on retrieval-based question answering systems in e-commerce

In this paper, we study transfer learning for the PI and NLI problems, aiming to propose a general framework, which can effectively and efficiently adapt the shared knowledge learned from a resource-rich source domain to a resource- poor target domain. Specifically, since most existing transfer learning methods only focus on learning a shared feature space across domains while ignoring the relationship between the source and target domains, we propose to simultaneously learn shared representations and domain relationships in a unified framework. Furthermore, we propose an efficient and effective hybrid model by combining a sentence encoding- based method and a sentence interaction-based method as our base model. Extensive experiments on both paraphrase identification and natural language inference demonstrate that our base model is efficient and has promising performance compared to the competing models, and our transfer learning method can help to significantly boost the performance. Further analysis shows that the inter-domain and intra-domain relationship captured by our model are insightful. Last but not least, we deploy our transfer learning model for PI into our online chatbot system, which can bring in significant improvements over our existing system. Finally, we launch our new system on the chatbot platform Eva in our E-commerce site AliExpress.Comment:

Detecting Dynamic Association among Twitter Topics

Over the last few years, Twitter is increasingly becoming an important source of up-to-date topics about what is happening in the world. In this paper, we propose a dynamic topic association detection model to discover relations between Twitter topics, by which users can gain insights into richer information about topics of interest. The proposed model utilizes a time constrained method to extract event-based spatio-temporal topic association, and constructs a dynamic temporal map to represent the obtained result. Experimental results show the improvement of the proposed model compared to static spatio-temporal method and co-occurrence method. Categories and Subject Descriptors: H.3.3 [Information storage and retrieval]: Information search and retrieval – Information filtering

How Shall We Catch People’s Concerns in Micro-blogging?

In micro-blogging, people talk about their daily life and change minds freely, thus by mining people’s interest in micro-blogging, we will easily perceive the pulse of society. In this paper, we catch what people are caring about in their daily life by discovering meaningful communities based on probabilistic factor model (PFM). The proposed solution identifies people’s interest from their friendship and content information. Therefore, it reveals the behaviors of people in micro-blogging naturally. Experimental results verify the effectiveness of the proposed model and show people’s social life vividly

Improving Dialogue Intent Classification with a Knowledge-Enhanced Multifactor Graph Model (Student Abstract)

Although current Graph Neural Network (GNN) based models achieved good performances in Dialogue Intent Classification (DIC), they leaf the inherent domain-specific knowledge out of consideration, leading to the lack of ability of acquiring fine-grained semantic information. In this paper, we propose a Knowledge-Enhanced Multifactor Graph (KEMG) Model for DIC. We firstly present a knowledge-aware utterance encoder with the help of a domain-specific knowledge graph, fusing token-level and entity-level semantic information, then design a heterogeneous dialogue graph encoder by explicitly modeling several factors that matter to contextual modeling of dialogues. Experiment results show that our proposed method outperforms other GNN-based methods on a dataset collected from a real-world online customer service dialogue system on the e-commerce website, JD

SimCTC: A Simple Contrast Learning Method of Text Clustering (Student Abstract)

This paper presents SimCTC, a simple contrastive learning (CL) framework that greatly advances the state-of-the-art text clustering models. In SimCTC, a pre-trained BERT model first maps the input sequence to the representation space, which is then followed by three different loss function heads: Clustering head, Instance-CL head and Cluster-CL head. Experimental results on multiple benchmark datasets demonstrate that SimCTC remarkably outperforms 6 competitive text clustering methods with 1%-6% improvement on Accuracy (ACC) and 1%-4% improvement on Normalized Mutual Information (NMI). Moreover, our results also show that the clustering performance can be further improved by setting an appropriate number of clusters in the cluster-level objective