Search Results for author:
Found 20 papers, 11 papers with code
ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations
While these tasks partially evaluate machines’ ability of narrative understanding, human-like reading comprehension requires the capability to process event-based information beyond arguments and temporal reasoning.
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents
Large Language Models (LLMs) have made significant progress in open-ended dialogue, yet their inability to retain and retrieve relevant information from long-term interactions limits their effectiveness in applications requiring sustained personalization.
Reverse Thinking Makes LLMs Stronger Reasoners
Reverse thinking plays a crucial role in human reasoning.
Speculative Knowledge Distillation: Bridging the Teacher-Student Gap Through Interleaved Sampling
To address these limitations, we introduce Speculative Knowledge Distillation (SKD), a novel approach that leverages cooperation between student and teacher models to generate high-quality training data on-the-fly while aligning with the student's inference-time distribution.
Dancing in Chains: Reconciling Instruction Following and Faithfulness in Language Models
Surprisingly, we find that less is more, as training ReSet with high-quality, yet substantially smaller data (three-fold less) yields superior results.
RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
Question answering based on retrieval augmented generation (RAG-QA) is an important research topic in NLP and has a wide range of real-world applications.
ACCENT: An Automatic Event Commonsense Evaluation Metric for Open-Domain Dialogue Systems
We take the first step by focusing on event commonsense that considers events and their relations, and is crucial in both dialogues and general commonsense reasoning.
Character-Centric Story Visualization via Visual Planning and Token Alignment
This task requires machines to 1) understand long text inputs and 2) produce a globally consistent image sequence that illustrates the contents of the story.
Go Back in Time: Generating Flashbacks in Stories with Event Temporal Prompts
Stories or narratives are comprised of a sequence of events.
ESTER: A Machine Reading Comprehension Dataset for Event Semantic Relation Reasoning
While these tasks partially evaluate machines' ability of narrative understanding, human-like reading comprehension requires the capability to process event-based information beyond arguments and temporal reasoning.
Modeling Context in Answer Sentence Selection Systems on a Latency Budget
In this work, we present an approach to efficiently incorporate contextual information in AS2 models.
Machine Reading Comprehension
Open-Domain Question Answering
+1
EventPlus: A Temporal Event Understanding Pipeline
We present EventPlus, a temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction.
ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning
While pre-trained language models (PTLMs) have achieved noticeable success on many NLP tasks, they still struggle for tasks that require event temporal reasoning, which is essential for event-centric applications.
Ranked #1 on
Question Answering
on Torque
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
There has been a steady need in the medical community to precisely extract the temporal relations between clinical events.
Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction
Extracting event temporal relations is a critical task for information extraction and plays an important role in natural language understanding.
TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions
A critical part of reading is being able to understand the temporal relationships between events described in a passage of text, even when those relationships are not explicitly stated.
Ranked #2 on
Question Answering
on Torque
Deep Structured Neural Network for Event Temporal Relation Extraction
We propose a novel deep structured learning framework for event temporal relation extraction.
Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction
We propose a joint event and temporal relation extraction model with shared representation learning and structured prediction.
Event Extraction
Joint Event and Temporal Relation Extraction
+4
Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding
In this work, we establish strong baselines for event temporal relation extraction on two under-explored story narrative datasets: Richer Event Description (RED) and Causal and Temporal Relation Scheme (CaTeRS).
Conditional Word Embedding and Hypothesis Testing via Bayes-by-Backprop
Conventional word embedding models do not leverage information from document meta-data, and they do not model uncertainty.
