Search Results for author:
Found 23 papers, 17 papers with code
Text Matching Improves Sequential Recommendation by Reducing Popularity Biases
TASTE alleviates the cold start problem by representing long-tail items using full-text modeling and bringing the benefits of pretrained language models to recommendation systems.
Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data
SANTA proposes two pretraining methods to make language models structure-aware and learn effective representations for structured data: 1) Structured Data Alignment, which utilizes the natural alignment relations between structured data and unstructured data for structure-aware pretraining.
Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval
Common IR pipelines are typically cascade systems that may involve multiple rankers and/or fusion models to integrate different information step-by-step.
CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network
However, recent studies on hypergraph learning that extend graph convolutional networks to hypergraphs cannot learn effectively from features of unlabeled data.
Universal Vision-Language Dense Retrieval: Learning A Unified Representation Space for Multi-Modal Retrieval
To learn a unified embedding space for multi-modal retrieval, UniVL-DR proposes two techniques: 1) Universal embedding optimization strategy, which contrastively optimizes the embedding space using the modality-balanced hard negatives; 2) Image verbalization method, which bridges the modality gap between images and texts in the raw data space.
Dimension Reduction for Efficient Dense Retrieval via Conditional Autoencoder
These embeddings need to be high-dimensional to fit training signals and guarantee the retrieval effectiveness of dense retrievers.
Ranked #1 on
Information Retrieval
on MS MARCO
P^3 Ranker: Mitigating the Gaps between Pre-training and Ranking Fine-tuning with Prompt-based Learning and Pre-finetuning
In this paper, we identify and study the two mismatches between pre-training and ranking fine-tuning: the training schema gap regarding the differences in training objectives and model architectures, and the task knowledge gap considering the discrepancy between the knowledge needed in ranking and that learned during pre-training.
A Roadmap for Big Model
With the rapid development of deep learning, training Big Models (BMs) for multiple downstream tasks becomes a popular paradigm.
YACLC: A Chinese Learner Corpus with Multidimensional Annotation
This resource is of great relevance for second language acquisition research, foreign-language teaching, and automatic grammatical error correction.
TIAGE: A Benchmark for Topic-Shift Aware Dialog Modeling
In research on dialog systems, the ability to actively and smoothly transition to new topics is often ignored.
More Robust Dense Retrieval with Contrastive Dual Learning
With contrastive learning, the dual training object of DANCE learns more tailored representations for queries and documents to keep the embedding space smooth and uniform, thriving on the ranking performance of DANCE on the MS MARCO document retrieval task.
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills.
Ranked #1 on
Grammatical Error Detection
on FCE
Few-Shot Conversational Dense Retrieval
In this paper, we present a Conversational Dense Retrieval system, ConvDR, that learns contextualized embeddings for multi-turn conversational queries and retrieves documents solely using embedding dot products.
OpenMatch: An Open Source Library for Neu-IR Research
OpenMatch is a Python-based library that serves for Neural Information Retrieval (Neu-IR) research.
Few-Shot Text Ranking with Meta Adapted Synthetic Weak Supervision
To democratize the benefits of Neu-IR, this paper presents MetaAdaptRank, a domain adaptive learning method that generalizes Neu-IR models from label-rich source domains to few-shot target domains.
CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search
Neural rankers based on deep pretrained language models (LMs) have been shown to improve many information retrieval benchmarks.
Adapting Open Domain Fact Extraction and Verification to COVID-FACT through In-Domain Language Modeling
With the epidemic of COVID-19, verifying the scientifically false online information, such as fake news and maliciously fabricated statements, has become crucial.
Toward Cross-Lingual Definition Generation for Language Learners
We demonstrate the effectiveness of this approach on zero-shot definition generation.
Capturing Global Informativeness in Open Domain Keyphrase Extraction
Open-domain KeyPhrase Extraction (KPE) aims to extract keyphrases from documents without domain or quality restrictions, e. g., web pages with variant domains and qualities.
Selective Weak Supervision for Neural Information Retrieval
This paper democratizes neural information retrieval to scenarios where large scale relevance training signals are not available.
Fine-grained Fact Verification with Kernel Graph Attention Network
Fact Verification requires fine-grained natural language inference capability that finds subtle clues to identify the syntactical and semantically correct but not well-supported claims.
Ranked #5 on
Fact Verification
on FEVER
Explore Entity Embedding Effectiveness in Entity Retrieval
Entity embedding learns lots of semantic information from the knowledge graph and represents entities with a low-dimensional representation, which provides an opportunity to establish interactions between query related entities and candidate entities for entity retrieval.
Entity-Duet Neural Ranking: Understanding the Role of Knowledge Graph Semantics in Neural Information Retrieval
This paper presents the Entity-Duet Neural Ranking Model (EDRM), which introduces knowledge graphs to neural search systems.
