Search Results for author:
Found 41 papers, 14 papers with code
From spoken dialogue to formal summary: An utterance rewriting for dialogue summarization
Firstly, an utterance rewriter is conducted to complete the ellipsis content of dialogue content and then obtain the rewriting utterances.
Interactive Latent Knowledge Selection for E-Commerce Product Copywriting Generation
As the multi-modal e-commerce is thriving, high-quality advertising product copywriting has gain more attentions, which plays a crucial role in the e-commerce recommender, advertising and even search platforms. The advertising product copywriting is able to enhance the user experience by highlighting the product’s characteristics with textual descriptions and thus to improve the likelihood of user click and purchase.
Learning Multi-Stage Multi-Grained Semantic Embeddings for E-Commerce Search
Retrieving relevant items that match users' queries from billion-scale corpus forms the core of industrial e-commerce search systems, in which embedding-based retrieval (EBR) methods are prevailing.
Automatic Scene-based Topic Channel Construction System for E-Commerce
To conduct scene marketing for e-commerce platforms, this work presents a novel product form, scene-based topic channel which typically consists of a list of diverse products belonging to the same usage scenario and a topic title that describes the scenario with marketing words.
Pre-training Tasks for User Intent Detection and Embedding Retrieval in E-commerce Search
BERT-style models pre-trained on the general corpus (e. g., Wikipedia) and fine-tuned on specific task corpus, have recently emerged as breakthrough techniques in many NLP tasks: question answering, text classification, sequence labeling and so on.
Context-Consistent Semantic Image Editing with Style-Preserved Modulation
Semantic image editing utilizes local semantic label maps to generate the desired content in the edited region.
Automatic Generation of Product-Image Sequence in E-commerce
For a platform with billions of products, it is extremely time-costly and labor-expensive to manually pick and organize qualified images.
Automatic Controllable Product Copywriting for E-Commerce
Automatic product description generation for e-commerce has witnessed significant advancement in the past decade.
Robust Meta-learning with Sampling Noise and Label Noise via Eigen-Reptile
Moreover, when handling the data with noisy labels, the meta-learner could be extremely sensitive to label noise on a corrupted dataset.
Meta Policy Learning for Cold-Start Conversational Recommendation
We propose to learn a meta policy and adapt it to new users with only a few trials of conversational recommendations.
Scenario-based Multi-product Advertising Copywriting Generation for E-Commerce
In this paper, we proposed an automatic Scenario-based Multi-product Advertising Copywriting Generation system (SMPACG) for E-Commerce, which has been deployed on a leading Chinese e-commerce platform.
Improving Long Tailed Document-Level Relation Extraction via Easy Relation Augmentation and Contrastive Learning
Towards real-world information extraction scenario, research of relation extraction is advancing to document-level relation extraction(DocRE).
Ranked #24 on
Relation Extraction
on DocRED
Reducing Flipping Errors in Deep Neural Networks
Deep neural networks (DNNs) have been widely applied in various domains in artificial intelligence including computer vision and natural language processing.
Givens Coordinate Descent Methods for Rotation Matrix Learning in Trainable Embedding Indexes
Product quantization (PQ) coupled with a space rotation, is widely used in modern approximate nearest neighbor (ANN) search systems to significantly compress the disk storage for embeddings and speed up the inner product computation.
Feeding What You Need by Understanding What You Learned
In this paper, we argue that a deep understanding of model capabilities and data properties can help us feed a model with appropriate training data based on its learning status.
Sequential Search with Off-Policy Reinforcement Learning
Recent years have seen a significant amount of interests in Sequential Recommendation (SR), which aims to understand and model the sequential user behaviors and the interactions between users and items over time.
Multiple Choice Questions based Multi-Interest Policy Learning for Conversational Recommendation
As a result, we first propose a more realistic CRS learning setting, namely Multi-Interest Multi-round Conversational Recommendation, where users may have multiple interests in attribute instance combinations and accept multiple items with partially overlapped combinations of attribute instances.
Intelligent Online Selling Point Extraction for E-Commerce Recommendation
In addition, this kind of product description should be eye-catching to the readers.
Automatic Product Copywriting for E-Commerce
It consists of two main components: 1) natural language generation, which is built from a transformer-pointer network and a pre-trained sequence-to-sequence model based on millions of training data from our in-house platform; and 2) copywriting quality control, which is based on both automatic evaluation and human screening.
DSGPT: Domain-Specific Generative Pre-Training of Transformers for Text Generation in E-commerce Title and Review Summarization
We propose a novel domain-specific generative pre-training (DS-GPT) method for text generation and apply it to the product titleand review summarization problems on E-commerce mobile display. First, we adopt a decoder-only transformer architecture, which fitswell for fine-tuning tasks by combining input and output all to-gether.
Learning to Generate Visual Questions with Noisy Supervision
The task of visual question generation (VQG) aims to generate human-like neural questions from an image and potentially other side information (e. g., answer type or the answer itself).
Graph-augmented Learning to Rank for Querying Large-scale Knowledge Graph
Finally, we apply an answer selection model on the full KSG and the top-ranked sub-KSGs respectively to validate the effectiveness of our proposed graph-augmented learning to rank method.
Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks
Most previous methods solve this task using a sequence-to-sequence model or using a graph-based model to encode RDF triples and to generate a text sequence.
Multi-behavior Graph Contextual Aware Network for Session-based Recommendation
Based on the global graph, MGCNet attaches the global interest representation to final item representation based on local contextual intention to address the limitation (iii).
Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation
In this work, we propose an end-to-end heterogeneous global graph learning framework, namely Graph Learning Augmented Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation.
Deep Natural Language Processing for LinkedIn Search
Building a successful search system requires a thorough understanding of textual data semantics, where deep learning based natural language processing techniques (deep NLP) can be of great help.
Deep Natural Language Processing for LinkedIn Search Systems
Many search systems work with large amounts of natural language data, e. g., search queries, user profiles and documents, where deep learning based natural language processing techniques (deep NLP) can be of great help.
Heterogeneous Global Graph Neural Networks for Personalized Session-based Recommendation
Additionally, existing personalized session-based recommenders capture user preference only based on the sessions of the current user, but ignore the useful item-transition patterns from other user's historical sessions.
SearchGCN: Powering Embedding Retrieval by Graph Convolution Networks for E-Commerce Search
Graph convolution networks (GCN), which recently becomes new state-of-the-art method for graph node classification, recommendation and other applications, has not been successfully applied to industrial-scale search engine yet.
Improving Sequential Recommendation Consistency with Self-Supervised Imitation
In this paper, we propose a model, SSI, to improve sequential recommendation consistency with Self-Supervised Imitation.
Graph Neural Networks for Natural Language Processing: A Survey
Deep learning has become the dominant approach in coping with various tasks in Natural LanguageProcessing (NLP).
Joint Learning of Deep Retrieval Model and Product Quantization based Embedding Index
Embedding index that enables fast approximate nearest neighbor(ANN) search, serves as an indispensable component for state-of-the-art deep retrieval systems.
Constructing Contrastive samples via Summarization for Text Classification with limited annotations
Unlike vision tasks, the data augmentation method for contrastive learning has not been investigated sufficiently in language tasks.
Query Rewriting via Cycle-Consistent Translation for E-Commerce Search
Online A/B experiments show that it improves core e-commerce business metrics significantly.
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search
We propose in this paper a novel Second-order Relevance, which is fundamentally different from the previous First-order Relevance, to improve result relevance prediction.
Are Interpretations Fairly Evaluated? A Definition Driven Pipeline for Post-Hoc Interpretability
Recent years have witnessed an increasing number of interpretation methods being developed for improving transparency of NLP models.
Deep Search Query Intent Understanding
Understanding a user's query intent behind a search is critical for modern search engine success.
Efficient Neural Query Auto Completion
On the candidate generation side, this system uses as much information as possible in unseen prefixes to generate relevant candidates, increasing the recall by a large margin.
DeText: A Deep Text Ranking Framework with BERT
Ranking is the most important component in a search system.
Memory-efficient Embedding for Recommendations
Specifically, we first proposed an end-to-end differentiable framework that can calculate the weights over various dimensions for feature fields in a soft and continuous manner with an AutoML based optimization algorithm; then we derive a hard and discrete embedding component architecture according to the maximal weights and retrain the whole recommender framework.
