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Found 26 papers, 8 papers with code
GLiREL -- Generalist Model for Zero-Shot Relation Extraction
We introduce GLiREL (Generalist Lightweight model for zero-shot Relation Extraction), an efficient architecture and training paradigm for zero-shot relation classification.
Ranked #1 on
Zero-shot Relation Classification
on FewRel
(Macro F1 metric)
STAGE: Simplified Text-Attributed Graph Embeddings Using Pre-trained LLMs
We present Simplified Text-Attributed Graph Embeddings (STAGE), a straightforward yet effective method for enhancing node features in Graph Neural Network (GNN) models that encode Text-Attributed Graphs (TAGs).
KGValidator: A Framework for Automatic Validation of Knowledge Graph Construction
This study explores the use of Large Language Models (LLMs) for automatic evaluation of knowledge graph (KG) completion models.
News Signals: An NLP Library for Text and Time Series
We present an open-source Python library for building and using datasets where inputs are clusters of textual data, and outputs are sequences of real values representing one or more time series signals.
Efficient Unsupervised Sentence Compression by Fine-tuning Transformers with Reinforcement Learning
Sentence compression reduces the length of text by removing non-essential content while preserving important facts and grammaticality.
DynE: Dynamic Ensemble Decoding for Multi-Document Summarization
Sequence-to-sequence (s2s) models are the basis for extensive work in natural language processing.
A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal
Multi-document summarization (MDS) aims to compress the content in large document collections into short summaries and has important applications in story clustering for newsfeeds, presentation of search results, and timeline generation.
Task Selection Policies for Multitask Learning
One of the questions that arises when designing models that learn to solve multiple tasks simultaneously is how much of the available training budget should be devoted to each individual task.
Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models
To our knowledge, this is the largest evaluation of multi-lingual translation yet conducted in terms of the total size of the training data we use, and in terms of the diversity of zero-shot translation pairs we evaluate.
Off-the-Shelf Unsupervised NMT
We frame unsupervised machine translation (MT) in the context of multi-task learning (MTL), combining insights from both directions.
Generating High-Quality Surface Realizations Using Data Augmentation and Factored Sequence Models
This work presents a new state of the art in reconstruction of surface realizations from obfuscated text.
Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation
This work presents a novel approach to Automatic Post-Editing (APE) and Word-Level Quality Estimation (QE) using ensembles of specialized Neural Machine Translation (NMT) systems.
Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search
Lexical constraints take the form of phrases or words that must be present in the output sequence.
Pushing the Limits of Translation Quality Estimation
Translation quality estimation is a task of growing importance in NLP, due to its potential to reduce post-editing human effort in disruptive ways.
MARMOT: A Toolkit for Translation Quality Estimation at the Word Level
The tool has a set of state-of-the-art features for QE, and new features can easily be added.
Modeling Language Proficiency Using Implicit Feedback
We describe the results of several experiments with interactive interfaces for native and L2 English students, designed to collect implicit feedback from students as they complete a reading activity.
