Existing neural methods have shown great potentials towards generating informative text from structured tabular data as well as maintaining high content fidelity. However, few of them shed light on generating personalized expressions, which often requires well-aligned persona-table-text datasets that are difficult to obtain. To overcome these obstacles, we explore personalized table-to-text generation under a zero-shot setting, by assuming no well-aligned persona-table-text triples are required during training. To this end, we firstly collect a set of unpaired persona information and then propose a semi-supervised approach with contrastive persona distillation (S2P-CPD) to generate personalized context. Specifically, tabular data and persona information are firstly represented as latent variables separately. Then, we devise a latent space fusion technique to distill persona information into the table representation. Besides, a contrastive-based discriminator is employed to guarantee the style consistency between the generated context and its corresponding persona. Experimental results on two benchmarks demonstrate S2P-CPD’s ability on keeping both content fidelity and personalized expressions.

Multi-modal Product Summary Generation is a new yet challenging task, which aims to generate a concise and readable summary for a product given its multi-modal content, e.g., its long text description and image. Although existing methods have achieved great success, they still suffer from three key limitations: 1) overlook the benefit of pre-training, 2) lack the representation-level supervision, and 3) ignore the diversity of the seller-generated data. To address these limitations, in this work, we propose a Vision-to-Prompt based multi-modal product summary generation framework, dubbed as V2P, where a Generative Pre-trained Language Model (GPLM) is adopted as the backbone. In particular, to maintain the original text capability of the GPLM and fully utilize the high-level concepts contained in the product image, we design V2P with two key components: vision-based prominent attribute prediction, and attribute prompt-guided summary generation. The first component works on obtaining the vital semantic attributes of the product from its image by the Swin Transformer, while the second component aims to generate the summary based on the product's long text description and the attribute prompts yielded by the first component with a GPLM. Towards comprehensive supervision over the second component, apart from the conventional output-level supervision, we introduce the representation-level regularization. Meanwhile, we design the data augmentation-based robustness regularization to handle the diverse inputs and improve the robustness of the second component. Extensive experiments on a large-scale Chinese dataset verify the superiority of our model over cutting-edge methods.

Qianglong Chen, Feng Ji, Xiangji Zeng, Feng-Lin Li, Ji Zhang, Haiqing Chen, Yin Zhang. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Existing data-driven methods can well handle short text generation. However, when applied to the long-text generation scenarios such as story generation or advertising text generation in the commercial scenario, these methods may generate illogical and uncontrollable texts. To address these aforementioned issues, we propose a graph-based grouping planner~(GGP) following the idea of first-plan-then-generate. Specifically, given a collection of key phrases, GGP firstly encodes these phrases into a instance-level sequential representation and a corpus-level graph-based representation separately. With these two synergic representations, we then regroup these phrases into a fine-grained plan, based on which we generate the final long text. We conduct our experiments on three long text generation datasets and the experimental results reveal that GGP significantly outperforms baselines, which proves that GGP can control the long text generation with knowing how to say and in what order.

User intent detection is vital for understanding their demands in dialogue systems. Although the User Intent Classification (UIC) task has been widely studied, for large-scale industrial applications, the task is still challenging. This is because user inputs in distinct domains may have different text distributions and target intent sets. When the underlying application evolves, new UIC tasks continuously emerge in a large quantity. Hence, it is crucial to develop a framework for large-scale extensible UIC that continuously fits new tasks and avoids catastrophic forgetting with an acceptable parameter growth rate. In this paper, we introduce the Meta Lifelong Learning (MeLL) framework to address this task. In MeLL, a BERT-based text encoder is employed to learn robust text representations across tasks, which is slowly updated for lifelong learning. We design global and local memory networks to capture the cross-task prototype representations of different classes, treated as the meta-learner quickly adapted to different tasks. Additionally, the Least Recently Used replacement policy is applied to manage the global memory such that the model size does not explode through time. Finally, each UIC task has its own task-specific output layer, with the attentive summarization of various features. We have conducted extensive experiments on both open-source and real industry datasets. Results show that MeLL improves the performance compared with strong baselines and also reduces the number of total parameters. We have also deployed MeLL on a real-world e-commerce dialogue system AliMe and observed significant improvements in terms of both F1 and the resources usage.

Due to the significant cognition reduction, multi-media content has become an increasingly important information type nowadays. More and more descriptions are coupled with images to make them more attractive and persuasive. Currently, several text-image retrieval methods have been developed to improve the efficiency of the time-consuming and professional process. However, in practical retrieval applications, it is the vivid and terse descriptions that are widely used, instead of the shallow captions that describe what is contained. Therefore, the most existing methods designed for the caption-style text can not achieve this purpose. To eliminate the mismatch, we introduce a novel problem about description-image retrieval and propose the specially designed method, named Adapted Graph Reasoning and Filtration (AGRF). In AGRF, we firstly leverage an adapted graph reasoning network to discover the combination of visual objects in the image. Then, a cross-modal gate mechanism is proposed to cast aside those description-independent combinations. Experiment results on the real-world dataset demonstrate the advantages of the AGRF over the state-of-the-art methods.

We present AliMe Avatar, a Vtuber designed for live-streaming sales in the E-commerce field. To support the emerging live shopping mode, the core of our digitial avatar is to enable customers to understand products and encourage customers to purchase in a virtual broadcasting room. Based on computer graphics & vision, natural language processing, and speech recognition & synthesis, our AI avatar is able to offer three kinds of key capabilities: custom appearance, product broadcasting, and multi-modal interaction. Currently, it has been launched online in the Taobao app, broadcasts 700+ hours and serves hundreds of thousands of customers per day. In this paper, we mainly focus on the product broadcasting part, demonstrate the system, present the underlying techniques, and share our experience in dealing with live-streaming E-commerce.

Cold-start is the most difficult and time-consuming phase when building a question answering based chatbot for a new business scenario because of the collection of sufficient training data. In this paper, we propose AliMe DA, a practical data augmentation (DA) framework that consists of data production, denoising and consumption, to alleviate this problem. We show how our DA approach can be used to substantially enhance annotation productivity and also improve downstream model performance. More importantly, we provide best practices for data augmentation, including how to choose and employ appropriate methods at each stage of our framework, and share our observation on the applicable scene of data augmentation in the era of pre-trained language models.

Different people have different habits of describing their intents in conversations. Some people tend to deliberate their intents in several successive utterances, i.e., they use several consistent messages for readability instead of a long sentence to express their question. This creates a predicament faced by the application of dialogue systems, especially in real-world industry scenarios, in which the dialogue system is unsure whether it should answer the user's query immediately or wait for user's further supplementary input. Inspired by such interesting quandary, we define a novel task: Wait-or-Answer to better tackle this dilemma faced by dialogue systems. We shed light on a new research topic about how the dialogue system can be more intelligent to behave in this Wait-or-Answer quandary. Further, we propose a predictive approach dubbed Predict-then-Decide (PTD) to resolve this Wait-or-Answer task. More specifically, we take advantage of a decision model to help the dialogue system decide to wait or answer. The decision of decision model is made with the assistance of two ancillary prediction models: a user prediction and an agent prediction. The user prediction model tries to predict what the user would supplement and uses its prediction to persuade the decision model that the user has some information to add, so the dialogue system should wait. The agent prediction model, nevertheless, struggles to predict the answer of the dialogue system and convince the decision model that it's a superior choice to answer the user's query immediately since the user's input is finished. We conduct our experiment on two real-life scenarios and three public datasets.Experimental results on five datasets show our proposed PTD approach significantly outperforms the existing models in solving this Wait-or-Answer problem.

How to build a high-quality multi-domain dialogue system is a challenging work due to its complicated and entangled dialogue state space among each domain, which seriously limits the quality of dialogue policy, and further affects the generated response. In this paper, we propose a novel method to acquire a satisfying policy and subtly circumvent the knotty dialogue state representation problem in the multi-domain setting. Inspired by real school teaching scenarios, our method is composed of multiple domain-specific teachers and a universal student. Each individual teacher only focuses on one specific domain and learns its corresponding domain knowledge and dialogue policy based on a precisely extracted single domain dialogue state representation. Then, these domain-specific teachers impart their domain knowledge and policies to a universal student model and collectively make this student model a multi-domain dialogue expert. Experiment results show that our method reaches competitive results with SOTAs in both multi-domain and single domain setting.

This paper aims to predict user satisfaction for customer service chatbot in session level, which is of great practical significance yet rather untouched. It requires to explore the relationship between questions and answers across different rounds of interactions, and handle user bias. We propose an approach to model multi-round conversations within one session and take user information into account. Experimental results on a dataset from a real-world industrial customer service chatbot Alime demonstrate the good performance of our proposed model.

Given a user query, traditional multi-turn retrieval-based dialogue systems first retrieve a set of candidate responses from the historical dialogue sessions. Then the response selection models select the most appropriate response to the given query. However, previous work only considers the matching between the query and the response but ignores the informative dialogue session in which the response is located. Nevertheless, this session, composed of the response, the response's history and the response's future, always contains valuable contextual information which can help the response selection task. More specifically, if the current query and a response's history both refer to the same question, we can conclude that this response is quite likely to answer this query. As for the response's future, it can always provide contextual hints and supplementary information that might be omitted in the response. Inspired by such motivation, we propose a query-to-session matching (QSM) framework to make full use of the session information: matching the query with the candidate session instead of the response only. Different from the previous work which ranks response directly, the response in the session with the highest query-to-session matching score will be selected as the desired response. In our proposed framework, the query, history, and future are all sequences of utterances, which makes it necessary to model the relationships among the utterances. So we propose a novel dialogue flow aware query-to-session matching (DF-QSM) model. The dialogue flows model the relationships among the utterances through a memory network. To our best knowledge, our paper is the first work to utilize both the response's history and future in the response selection task. The experimental results on three multi-turn response selection benchmarks show that our proposed model outperforms existing state-of-the-art methods by a large margin.

Pre-­sales customer service is of importance to E­-commerce plat­forms as it contributes to optimizing customers? buying process. To better serve users, we propose AliMe KG, a domain knowledge graph in E­-commerce that captures user problems, points of inter­est (POI), item information and relations thereof. It helps to under­ stand user needs, answer pre­-sales questions and generate explana­tion texts. We applied AliMe KG to several online business scenar­ios such as shopping guide, question answering over properties and selling point generation, and gained positive and beneficial business results. In the paper, we systematically introduce how we construct domain knowledge graph from free text, and demonstrate its busi­ness value with several applications. Our experience shows that min­ ing structured knowledge from free text in vertical domain is prac­ticable, and can be of substantial value in industrial settings.

In order to facilitate natural language understanding, the key is to engage commonsense or background knowledge.However, how to engage commonsense effectively in question answering systems is still under exploration in both research academia and industry.In this paper, we propose a novel question-answering method by integrating multiple knowledge sources, i.e.Con-ceptNet, Wikipedia, and the Cambridge Dictionary, to boost the performance.More concretely, we first introduce a novel graph-based iterative knowledge retrieval module, which iteratively retrieves concepts and entities related to the given question and its choices from multiple knowledge sources.Afterward, we use a pre-trained language model to encode the question, retrieved knowledge and choices, and propose an answer choice-aware attention mechanism to fuse all hidden representations of the previous modules.Finally, the linear classifier for specific tasks is used to predict the answer.Experimental results on the CommonsenseQA dataset show that our method significantly outperforms other competitive methods and achieves the new state-ofthe-art.In addition, further ablation studies demonstrate the effectiveness of our graph-based iterative knowledge retrieval module and the answer choice-aware attention module in retrieving and synthesizing background knowledge from multiple knowledge sources.

We study the retrieval-based multi-turn information-seeking dialogue systems, which are widely used in many scenarios. Most of the previous works select the response according to the matching degree between the query's context and the candidate responses. Though great progress has been made, existing works ignore the contexts of the responses, which could provide rich information for selecting the most appropriate response. The more similar the query's context and certain response's context are, the more likely they are to indicate the same question, and thus, the more likely this response is to answer the query. In this paper, we consider the response and its context as a whole session and explore the task of matching the query's context with the sessions. More specifically, we propose to match between the query's context and response's context and integrate the context-to-context matching with context-to-response matching. Experiment results prove that our proposed context-to-session method outperforms the strong baselines significantly.

Generating natural language descriptions for knowledge graph (KG) is an important category for intelligent writing. Recent models on this task substitute the sequence encoder in a commonly used encoder-decoder framework with a graph encoder. However, these models suffer from entity missing and repetition. In this paper, we propose a novel end-to-end generation model named G2T, which integrates a novel Graph Structure Enhanced Mechanism (GSEM) and a Copy Coverage Loss (CCL). Instead of just considering graph structure in the encoding phase in most existing methods, our GSEM fully utilizes graph structure in the decoding phase and helps to mitigate entity missing problem. Moreover, our CCL can further improve performance by avoiding generating repeated entities. With their help, our model is capable of generating fluent description for KG. The results of automatic and human evaluations show that our model outperforms the state-of-the-art models.

Personal assistant systems, such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana, are becoming ever more widely used. Understanding user intent such as clarification questions, potential answers and user feedback in information-seeking conversations is critical for retrieving good responses. In this paper, we analyze user intent patterns in information-seeking conversations and propose an intent-aware neural response ranking model “IART”, which refers to “Intent-Aware Ranking with Transformers”. IART is built on top of the integration of user intent modeling and language representation learning with the Transformer architecture, which relies entirely on a self-attention mechanism instead of recurrent nets [35]. It incorporates intent-aware utterance attention to derive an importance weighting scheme of utterances in conversation context with the aim of better conversation history understanding. We conduct extensive experiments with three information-seeking conversation data sets including both standard benchmarks and commercial data. Our proposed model outperforms all baseline methods with respect to a variety of metrics. We also perform case studies and analysis of learned user intent and its impact on response ranking in information-seeking conversations to provide interpretation of results.

With the large quantity of textual information produced on the Internet, a critical necessity is to filter out the irrelevant information and organize the rest into categories of interest (e.g., an emerging event). However, supervised-learning document filtering methods heavily rely on a large number of labeled documents for model training. Manually identifying plenty of positive examples for each category is expensive and time-consuming. Also, it is unrealistic to cover all the categories from an evolving text source that covers diverse kinds of events, user opinions, and daily life activities. In this article, we propose a novel attention-based deep relevance model for few-shot document filtering (named ADRM), inspired by the relevance feedback methodology proposed for ad hoc retrieval. ADRM calculates the relevance score between a document and a category by taking a set of seed words and a few seed documents relevant to the category. It constructs the category-specific conceptual representation of the document based on the corresponding seed words and seed documents. Specifically, to filter irrelevant yet noisy information in the seed documents, ADRM employs two types of attention mechanisms (namely whole-match attention and max-match attention ) and generates category-specific representations for them. Then ADRM is devised to extract the relevance signals by modeling the hidden feature interactions in the word embedding space. The relevance signals are extracted through a gated convolutional process, a self-attention layer, and a relevance aggregation layer. Extensive experiments on three real-world datasets show that ADRM consistently outperforms the existing technical alternatives, including the conventional classification and retrieval baselines, and the state-of-the-art deep relevance ranking models for few-shot document filtering. We also perform an ablation study to demonstrate that each component in ADRM is effective for enhancing filtering performance. Further analysis shows that ADRM is robust under varying parameter settings.

A fundamental trade-off between effectiveness and efficiency needs to be balanced when designing an online question answering system. Effectiveness comes from sophisticated functions such as extractive machine reading comprehension (MRC), while efficiency is obtained from improvements in preliminary retrieval components such as candidate document selection and paragraph ranking. Given the complexity of the real-world multi-document MRC scenario, it is difficult to jointly optimize both in an end-to-end system. To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the documentlevel and paragraph-level ranking of candidate texts to more precise answer extraction with machine reading comprehension. Specifically, irrelevant documents and paragraphs are first filtered out with simple functions for efficiency consideration. Then we jointly train three modules on the remaining texts for better tracking the answer: the document extraction, the paragraph extraction and the answer extraction. Experiment results show that the proposed method outperforms the previous state-of-the-art methods on two large-scale multidocument benchmark datasets, i.e., TriviaQA and DuReader. In addition, our online system can stably serve typical scenarios with millions of daily requests in less than 50ms.

In this paper, we present a fast and strong neural approach for general purpose text matching applications.We explore what is sufficient to build a fast and well-performed text matching model and propose to keep three key features available for inter-sequence alignment: original point-wise features, previous aligned features, and contextual features while simplifying all the remaining components.We conduct experiments on four well-studied benchmark datasets across tasks of natural language inference, paraphrase identification and answer selection.The performance of our model is on par with the state-of-the-art on all datasets with much fewer parameters and the inference speed is at least 6 times faster compared with similarly performed ones.

Information-seeking conversation system aims at satisfying the information needs of users through conversations. Text matching between a user query and a pre-collected question is an important part of the information-seeking conversation in E-commerce. In the practical scenario, a sort of questions always correspond to a same answer. Naturally, these questions can form a bag. Learning the matching between user query and bag directly may improve the conversation performance, denoted as query-bag matching. Inspired by such opinion, we propose a query-bag matching model which mainly utilizes the mutual coverage between query and bag and measures the degree of the content in the query mentioned by the bag, and vice verse. In addition, the learned bag representation in word level helps find the main points of a bag in a fine grade and promotes the query-bag matching performance. Experiments on two datasets show the effectiveness of our model.

Zehao Lin, Xinjing Huang, Feng Ji, Haiqing Chen, Yin Zhang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

The users often have many product-related questions before they make a purchase decision in E-commerce. However, it is often time-consuming to examine each user review to identify the desired information. In this paper, we propose a novel review-driven framework for answer generation for product-related questions in E-commerce, named RAGE. We develope RAGE on the basis of the multi-layer convolutional architecture to facilitate speed-up of answer generation with the parallel computation. For each question, RAGE first extracts the relevant review snippets from the reviews of the corresponding product. Then, we devise a mechanism to identify the relevant information from the noise-prone review snippets and incorporate this information to guide the answer generation. The experiments on two real-world E-Commerce datasets show that the proposed RAGE significantly outperforms the existing alternatives in producing more accurate and informative answers in natural language. Moreover, RAGE takes much less time for both model training and answer generation than the existing RNN based generation models.

Deep text matching approaches have been widely studied for many applications including question answering and information retrieval systems. To deal with a domain that has insufficient labeled data, these approaches can be used in a Transfer Learning (TL) setting to leverage labeled data from a resource-rich source domain. To achieve better performance, source domain data selection is essential in this process to prevent the "negative transfer" problem. However, the emerging deep transfer models do not fit well with most existing data selection methods, because the data selection policy and the transfer learning model are not jointly trained, leading to sub-optimal training efficiency. In this paper, we propose a novel reinforced data selector to select high-quality source domain data to help the TL model. Specifically, the data selector "acts" on the source domain data to find a subset for optimization of the TL model, and the performance of the TL model can provide "rewards" in turn to update the selector. We build the reinforced data selector based on the actor-critic framework and integrate it to a DNN based transfer learning model, resulting in a Reinforced Transfer Learning (RTL) method. We perform a thorough experimental evaluation on two major tasks for text matching, namely, paraphrase identification and natural language inference. Experimental results show the proposed RTL can significantly improve the performance of the TL model. We further investigate different settings of states, rewards, and policy optimization methods to examine the robustness of our method. Last, we conduct a case study on the selected data and find our method is able to select source domain data whose Wasserstein distance is close to the target domain data. This is reasonable and intuitive as such source domain data can provide more transferability power to the model.

Dialogue management (DM) is responsible for predicting the next action of a dialogue system according to the current dialogue state and thus plays a central role in task-oriented dialogue systems. Since DM requires having access not only to local utterances but also to the global semantics of the entire dialogue session, modeling the long-range history information is a critical issue. To this end, we propose MAD, a novel memory-augmented dialogue management model that employs a memory controller and two additional memory structures (i.e., a slot-value memory and an external memory). The slot-value memory tracks the dialogue state by memorizing and updating the values of semantic slots (i.e., cuisine, price, and location), and the external memory augments the representation of hidden states of traditional recurrent neural networks by storing more context information. To update the dialogue state efficiently, we also propose slot-level attention on user utterances to extract specific semantic information for each slot. Experiments show that our model can obtain state-of-the-art performance and outperforms existing baselines.

In the era of big data, focused analysis for diverse topics with a short response time becomes an urgent demand.As a fundamental task, information filtering therefore becomes a critical necessity.In this paper, we propose a novel deep relevance model for zero-shot document filtering, named DAZER.DAZER estimates the relevance between a document and a category by taking a small set of seed words relevant to the category.With pre-trained word embeddings from a large external corpus, DAZER is devised to extract the relevance signals by modeling the hidden feature interactions in the word embedding space.The relevance signals are extracted through a gated convolutional process.The gate mechanism controls which convolution filters output the relevance signals in a category dependent manner.Experiments on two document collections of two different tasks (i.e., topic categorization and sentiment analysis) demonstrate that DAZER significantly outperforms the existing alternative solutions, including the state-of-the-art deep relevance ranking models.

Existing approaches to neural machine translation are typically autoregressive models.While these models attain state-of-the-art translation quality, they are suffering from low parallelizability and thus slow at decoding long sequences.In this paper, we propose a novel model for fast sequence generationthe semi-autoregressive Transformer (SAT).The SAT keeps the autoregressive property in global but relieves in local and thus are able to produce multiple successive words in parallel at each time step.Experiments conducted on English-German and Chinese-English translation tasks show that the SAT achieves a good balance between translation quality and decoding speed.On WMT'14 English-German translation, the SAT achieves 5.58× speedup while maintaining 88% translation quality, significantly better than the previous non-autoregressive methods.When produces two words at each time step, the SAT is almost lossless (only 1% degeneration in BLEU score).

Topic structure analysis plays a pivotal role in dialogue understanding. We propose a reinforcement learning (RL) method for topic segmentation and labeling in goal-oriented dialogues, which aims to detect topic boundaries among dialogue utterances and assign topic labels to the utterances. We address three common issues in the goal-oriented customer service dialogues: informality, local topic continuity, and global topic structure. We explore the task in a weakly supervised setting and formulate it as a sequential decision problem. The proposed method consists of a state representation network to address the informality issue, and a policy network with rewards to model local topic continuity and global topic structure. To train the two networks and offer a warm-start to the policy, we firstly use some keywords to annotate the data automatically. We then pre-train the networks on noisy data. Henceforth, the method continues to refine the data labels using the current policy to learn better state representations on the refined data for obtaining a better policy. Results demonstrate that this weakly supervised method obtains substantial improvements over state-of-the-art baselines.

Intelligent personal assistant systems with either text-based or voice-based conversational interfaces are becoming increasingly popular around the world. Retrieval-based conversation models have the advantages of returning fluent and informative responses. Most existing studies in this area are on open domain ''chit-chat'' conversations or task / transaction oriented conversations. More research is needed for information-seeking conversations. There is also a lack of modeling external knowledge beyond the dialog utterances among current conversational models. In this paper, we propose a learning framework on the top of deep neural matching networks that leverages external knowledge for response ranking in information-seeking conversation systems. We incorporate external knowledge into deep neural models with pseudo-relevance feedback and QA correspondence knowledge distillation. Extensive experiments with three information-seeking conversation data sets including both open benchmarks and commercial data show that, our methods outperform various baseline methods including several deep text matching models and the state-of-the-art method on response selection in multi-turn conversations. We also perform analysis over different response types, model variations and ranking examples. Our models and research findings provide new insights on how to utilize external knowledge with deep neural models for response selection and have implications for the design of the next generation of information-seeking conversation systems.

Nowadays, it is a heated topic for many industries to build automatic question-answering (QA) systems. A key solution to these QA systems is to retrieve from a QA knowledge base the most similar question of a given question, which can be reformulated as a paraphrase identification (PI) or a natural language inference (NLI) problem. However, most existing models for PI and NLI have at least two problems: They rely on a large amount of labeled data, which is not always available in real scenarios, and they may not be efficient for industrial applications. 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.

We present AliMe Assist, an intelligent assistant designed for creating an innovative online shopping experience in E-commerce. Based on question answering (QA), AliMe Assist offers assistance service, customer service, and chatting service. It is able to take voice and text input, incorporate context to QA, and support multi-round interaction. Currently, it serves millions of customer questions per day and is able to address 85% of them. In this paper, we demonstrate the system, present the underlying techniques, and share our experience in dealing with real-world QA in the E-commerce field.