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Found 15 papers, 6 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.
Out of Style: RAG's Fragility to Linguistic Variation
Despite the impressive performance of Retrieval-augmented Generation (RAG) systems across various NLP benchmarks, their robustness in handling real-world user-LLM interaction queries remains largely underexplored.
DCatalyst: A Unified Accelerated Framework for Decentralized Optimization
We introduce DCatalyst, a unified black-box framework that integrates Nesterov acceleration into decentralized optimization algorithms.
Enhancing Convergence of Decentralized Gradient Tracking under the KL Property
We study decentralized multiagent optimization over networks, modeled as undirected graphs.
Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models
Shopping MMLU consists of 57 tasks covering 4 major shopping skills: concept understanding, knowledge reasoning, user behavior alignment, and multi-linguality, and can thus comprehensively evaluate the abilities of LLMs as general shop assistants.
Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification
Leveraging the contextual priors of MVD at both the data and label levels, we propose a novel consistency learning framework Con4m, which effectively utilizes contextual information more conducive to discriminating consecutive segments in segmented TSC tasks, while harmonizing inconsistent boundary labels for training.
Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reports
In this paper, we use financial report summarization as a case study because financial reports are not only long but also use numbers and tables extensively.
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.
