Search Results for author:
Found 10 papers, 4 papers with code
MetaTS: Meta Teacher-Student Network for Multilingual Sequence Labeling with Minimal Supervision
In this work, we explore multilingual sequence labeling with minimal supervision using a single unified model for multiple languages.
Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reports
We propose a computational framework for characterizing multimodal long-form summarization and investigate the behavior of Claude 2. 0/2. 1, GPT-4/3. 5, and Command.
Enhancing User Intent Capture in Session-Based Recommendation with Attribute Patterns
The goal of session-based recommendation in E-commerce is to predict the next item that an anonymous user will purchase based on the browsing and purchase history.
Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning
This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages by transfering knowledge from TKGs in high-resource ones.
FolkScope: Intention Knowledge Graph Construction for E-commerce Commonsense Discovery
Understanding users' intentions in e-commerce platforms requires commonsense knowledge.
Short Text Pre-training with Extended Token Classification for E-commerce Query Understanding
When applying masking to short search queries, most contextual information is lost and the intent of the search queries may be changed.
Multilingual Knowledge Graph Completion with Self-Supervised Adaptive Graph Alignment
In this paper, we explore multilingual KG completion, which leverages limited seed alignment as a bridge, to embrace the collective knowledge from multiple languages.
Ranked #3 on
Knowledge Graph Completion
on DPB-5L (French)
Acceleration in Distributed Optimization under Similarity
In order to reduce the number of communications to reach a solution accuracy, we proposed a {\it preconditioned, accelerated} distributed method.
QUEACO: Borrowing Treasures from Weakly-labeled Behavior Data for Query Attribute Value Extraction
We study the problem of query attribute value extraction, which aims to identify named entities from user queries as diverse surface form attribute values and afterward transform them into formally canonical forms.
Named Entity Recognition with Small Strongly Labeled and Large Weakly Labeled Data
Unfortunately, we observe that weakly labeled data does not necessarily improve, or even deteriorate the model performance (due to the extensive noise in the weak labels) when we train deep NER models over a simple or weighted combination of the strongly labeled and weakly labeled data.
