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Found 34 papers, 9 papers with code
Turn-Level User Satisfaction Estimation in E-commerce Customer Service
To this end, we formalize the turn-level satisfaction estimation as a reinforcement learning problem, in which the model can be optimized with only session-level satisfaction labels.
CASEIN: Cascading Explicit and Implicit Control for Fine-grained Emotion Intensity Regulation
However, these high-level semantic probabilities are often inaccurate and unsmooth at the phoneme level, leading to bias in learning.
Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation
Specifically, tabular data and persona information are firstly represented as latent variables separately.
Knowledge Distillation of Transformer-based Language Models Revisited
In the past few years, transformer-based pre-trained language models have achieved astounding success in both industry and academia.
GGP: A Graph-based Grouping Planner for Explicit Control of Long Text Generation
With these two synergic representations, we then regroup these phrases into a fine-grained plan, based on which we generate the final long text.
SPMoE: Generate Multiple Pattern-Aware Outputs with Sparse Pattern Mixture of Experts
Each one-to-one mapping is associated with a conditional generation pattern and is modeled with an expert in SPMoE.
KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference
In order to better understand the reason behind model behaviors (i. e., making predictions), most recent works have exploited generative models to provide complementary explanations.
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots
E-commerce has grown substantially over the last several years, and chatbots for intelligent customer service are concurrently drawing attention.
An Emotion-controlled Dialog Response Generation Model with Dynamic Vocabulary
In response generation task, proper sentimental expressions can obviously improve the human-like level of the responses.
OneStop QAMaker: Extract Question-Answer Pairs from Text in a One-Stop Approach
This pipeline approach, however, is undesired in mining the most appropriate QA pairs from documents since it ignores the connection between question generation and answer extraction, which may lead to incompatible QA pair generation, i. e., the selected answer span is inappropriate for question generation.
Improving Commonsense Question Answering by Graph-based Iterative Retrieval over Multiple Knowledge Sources
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.
AliMe KG: Domain Knowledge Graph Construction and Application in E-commerce
Pre-sales customer service is of importance to E-commerce platforms as it contributes to optimizing customers' buying process.
Predict-then-Decide: A Predictive Approach for Wait or Answer Task in Dialogue Systems
More specifically, we take advantage of a decision model to help the dialogue system decide whether to wait or answer.
MTSS: Learn from Multiple Domain Teachers and Become a Multi-domain Dialogue Expert
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.
MLR: A Two-stage Conversational Query Rewriting Model with Multi-task Learning
In this paper, we propose the conversational query rewriting model - MLR, which is a Multi-task model on sequence Labeling and query Rewriting.
"Wait, I'm Still Talking!" Predicting the Dialogue Interaction Behavior Using Imagine-Then-Arbitrate Model
And the arbitrator decides whether to wait or to make a response to the user directly.
IART: Intent-aware Response Ranking with Transformers in Information-seeking Conversation Systems
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.
Transformation of Dense and Sparse Text Representations
The key idea of the proposed approach is to use a Forward Transformation to transform dense representations to sparse representations.
Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce
Information-seeking conversation system aims at satisfying the information needs of users through conversations.
Task-Oriented Conversation Generation Using Heterogeneous Memory Networks
How to incorporate external knowledge into a neural dialogue model is critically important for dialogue systems to behave like real humans.
Learning from Positive and Unlabeled Data with Adversarial Training
Positive-unlabeled (PU) learning learns a binary classifier using only positive and unlabeled examples without labeled negative examples.
Teacher-Student Framework Enhanced Multi-domain Dialogue Generation
Dialogue systems dealing with multi-domain tasks are highly required.
Simple and Effective Text Matching with Richer Alignment Features
In this paper, we present a fast and strong neural approach for general purpose text matching applications.
Ranked #5 on
Natural Language Inference
on SciTail
Review-Driven Answer Generation for Product-Related Questions in E-Commerce
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.
Learning to Selectively Transfer: Reinforced Transfer Learning for Deep Text Matching
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.
A Deep Cascade Model for Multi-Document Reading Comprehension
To address this problem, we develop a novel deep cascade learning model, which progressively evolves from the document-level and paragraph-level ranking of candidate texts to more precise answer extraction with machine reading comprehension.
Ranked #2 on
Question Answering
on MS MARCO
Semi-Autoregressive Neural Machine Translation
Existing approaches to neural machine translation are typically autoregressive models.
A Deep Relevance Model for Zero-Shot Document Filtering
In the era of big data, focused analysis for diverse topics with a short response time becomes an urgent demand.
Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce
Building multi-turn information-seeking conversation systems is an important and challenging research topic.
Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
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.
Memory-augmented Dialogue Management for Task-oriented Dialogue Systems
Dialogue management (DM) decides the next action of a dialogue system according to the current dialogue state, and thus plays a central role in task-oriented dialogue systems.
AliMe Assist: An Intelligent Assistant for Creating an Innovative E-commerce Experience
We present AliMe Assist, an intelligent assistant designed for creating an innovative online shopping experience in E-commerce.
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.
AliMe Chat: A Sequence to Sequence and Rerank based Chatbot Engine
We propose AliMe Chat, an open-domain chatbot engine that integrates the joint results of Information Retrieval (IR) and Sequence to Sequence (Seq2Seq) based generation models.
