Search Results for author:
Found 26 papers, 12 papers with code
End-to-End Chinese Speaker Identification
End-to-end SI systems, on the other hand, are not limited by individual modules, but suffer from insufficient training data from the existing small-scale datasets.
RESIN-11: Schema-guided Event Prediction for 11 Newsworthy Scenarios
We introduce RESIN-11, a new schema-guided event extraction&prediction framework that can be applied to a large variety of newsworthy scenarios.
New Frontiers of Information Extraction
This tutorial targets researchers and practitioners who are interested in AI and ML technologies for structural information extraction (IE) from unstructured textual sources.
There’s a Time and Place for Reasoning Beyond the Image
Images are often more significant than only the pixels to human eyes, as we can infer, associate, and reason with contextual information from other sources to establish a more complete picture.
A Meta-framework for Spatiotemporal Quantity Extraction from Text
News events are often associated with quantities (e. g., the number of COVID-19 patients or the number of arrests in a protest), and it is often important to extract their type, time, and location from unstructured text in order to analyze these quantity events.
Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking
While large language models (LLMs) have demonstrated increasing power, they have also given rise to a wide range of harmful behaviors.
Deceiving Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination?
During the construction of the evidence, we purposefully replace semantic clues (entities) that may lead to the correct answer with distractor clues (evidence) that will not directly lead to the correct answer but require a chain-like reasoning process.
Sub-Sentence Encoder: Contrastive Learning of Propositional Semantic Representations
We introduce sub-sentence encoder, a contrastively-learned contextual embedding model for fine-grained semantic representation of text.
Building Interpretable and Reliable Open Information Retriever for New Domains Overnight
Information retrieval (IR) or knowledge retrieval, is a critical component for many down-stream tasks such as open-domain question answering (QA).
Interpretable by Design Visual Question Answering
Model interpretability has long been a hard problem for the AI community especially in the multimodal setting, where vision and language need to be aligned and reasoned at the same time.
Generic Temporal Reasoning with Differential Analysis and Explanation
Temporal reasoning is the task of predicting temporal relations of event pairs.
Learning to Decompose: Hypothetical Question Decomposition Based on Comparable Texts
Explicit decomposition modeling, which involves breaking down complex tasks into more straightforward and often more interpretable sub-tasks, has long been a central theme in developing robust and interpretable NLU systems.
Cross-Lingual Speaker Identification Using Distant Supervision
Speaker identification, determining which character said each utterance in literary text, benefits many downstream tasks.
There is a Time and Place for Reasoning Beyond the Image
For example, in Figure 1, we can find a way to identify the news articles related to the picture through segment-wise understandings of the signs, the buildings, the crowds, and more.
RESIN: A Dockerized Schema-Guided Cross-document Cross-lingual Cross-media Information Extraction and Event Tracking System
We present a new information extraction system that can automatically construct temporal event graphs from a collection of news documents from multiple sources, multiple languages (English and Spanish for our experiment), and multiple data modalities (speech, text, image and video).
Temporal Reasoning on Implicit Events from Distant Supervision
We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which systems understand implicit events -- events that are not mentioned explicitly in natural language text but can be inferred from it.
Evaluating NLP Models via Contrast Sets
Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.
Temporal Common Sense Acquisition with Minimal Supervision
Temporal common sense (e. g., duration and frequency of events) is crucial for understanding natural language.
Cross-lingual Entity Alignment with Incidental Supervision
Much research effort has been put to multilingual knowledge graph (KG) embedding methods to address the entity alignment task, which seeks to match entities in different languagespecific KGs that refer to the same real-world object.
Ranked #19 on
Entity Alignment
on DBP15k zh-en
Evaluating Models' Local Decision Boundaries via Contrast Sets
Unfortunately, when a dataset has systematic gaps (e. g., annotation artifacts), these evaluations are misleading: a model can learn simple decision rules that perform well on the test set but do not capture a dataset's intended capabilities.
``Going on a vacation'' takes longer than ``Going for a walk'': A Study of Temporal Commonsense Understanding
Understanding time is crucial for understanding events expressed in natural language.
"Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding
Understanding time is crucial for understanding events expressed in natural language.
Zero-Shot Open Entity Typing as Type-Compatible Grounding
We evaluate our system on a broad range of datasets, including standard fine-grained and coarse-grained entity typing datasets, and also a dataset in the biological domain.
CogCompTime: A Tool for Understanding Time in Natural Language Text
Automatic extraction of temporal information in text is an important component of natural language understanding.
CogCompTime: A Tool for Understanding Time in Natural Language
Automatic extraction of temporal information is important for natural language understanding.
