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Found 20 papers, 12 papers with code
Reading the Manual: Event Extraction as Definition Comprehension
We ask whether text understanding has progressed to where we may extract event information through incremental refinement of bleached statements derived from annotation manuals.
Hierarchical Entity Typing via Multi-level Learning to Rank
We propose a novel method for hierarchical entity classification that embraces ontological structure at both training and during prediction.
Joint Modeling of Arguments for Event Understanding
We recognize the task of event argument linking in documents as similar to that of intent slot resolution in dialogue, providing a Transformer-based model that extends from a recently proposed solution to resolve references to slots.
Pattern-aware Data Augmentation for Query Rewriting in Voice Assistant Systems
Query rewriting (QR) systems are widely used to reduce the friction caused by errors in a spoken language understanding pipeline.
LOME: Large Ontology Multilingual Extraction
We present LOME, a system for performing multilingual information extraction.
Everything Is All It Takes: A Multipronged Strategy for Zero-Shot Cross-Lingual Information Extraction
Zero-shot cross-lingual information extraction (IE) describes the construction of an IE model for some target language, given existing annotations exclusively in some other language, typically English.
Asking the Right Questions in Low Resource Template Extraction
Information Extraction (IE) researchers are mapping tasks to Question Answering (QA) in order to leverage existing large QA resources, and thereby improve data efficiency.
Iterative Document-level Information Extraction via Imitation Learning
We present a novel iterative extraction model, IterX, for extracting complex relations, or templates (i. e., N-tuples representing a mapping from named slots to spans of text) within a document.
An Empirical Study on Finding Spans
We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks.
On Event Individuation for Document-Level Information Extraction
As information extraction (IE) systems have grown more adept at processing whole documents, the classic task of template filling has seen renewed interest as benchmark for document-level IE.
When Do Decompositions Help for Machine Reading?
Answering complex questions often requires multi-step reasoning in order to obtain the final answer.
Condensing Multilingual Knowledge with Lightweight Language-Specific Modules
Incorporating language-specific (LS) modules is a proven method to boost performance in multilingual machine translation.
Differentiable Tree Operations Promote Compositional Generalization
To facilitate the learning of these symbolic sequences, we introduce a differentiable tree interpreter that compiles high-level symbolic tree operations into subsymbolic matrix operations on tensors.
A Unified View of Evaluation Metrics for Structured Prediction
We present a conceptual framework that unifies a variety of evaluation metrics for different structured prediction tasks (e. g. event and relation extraction, syntactic and semantic parsing).
FAITHSCORE: Evaluating Hallucinations in Large Vision-Language Models
We introduce FAITHSCORE (Faithfulness to Atomic Image Facts Score), a reference-free and fine-grained evaluation metric that measures the faithfulness of the generated free-form answers from large vision-language models (LVLMs).
Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles
Large language models trained primarily in a monolingual setting have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-context learning.
Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation
However, even the top-performing 13B LLM-based translation models, like ALMA, does not match the performance of state-of-the-art conventional encoder-decoder translation models or larger-scale LLMs such as GPT-4.
The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts
As the influence of large language models (LLMs) spans across global communities, their safety challenges in multilingual settings become paramount for alignment research.
MultiMUC: Multilingual Template Filling on MUC-4
We introduce MultiMUC, the first multilingual parallel corpus for template filling, comprising translations of the classic MUC-4 template filling benchmark into five languages: Arabic, Chinese, Farsi, Korean, and Russian.
Streaming Sequence Transduction through Dynamic Compression
We introduce STAR (Stream Transduction with Anchor Representations), a novel Transformer-based model designed for efficient sequence-to-sequence transduction over streams.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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