Search Results for author:
Found 17 papers, 7 papers with code
Graph Representation of Narrative Context: Coherence Dependency via Retrospective Questions
This work introduces a novel and practical paradigm for narrative comprehension, stemming from the observation that individual passages within narratives are often cohesively related than being isolated.
Identifying Factual Inconsistency in Summaries: Towards Effective Utilization of Large Language Model
Factual inconsistency poses a significant hurdle for the commercial deployment of abstractive summarizers.
Previously on the Stories: Recap Snippet Identification for Story Reading
Similar to the "previously-on" scenes in TV shows, recaps can help book reading by recalling the readers' memory about the important elements in previous texts to better understand the ongoing plot.
SIG: Speaker Identification in Literature via Prompt-Based Generation
Identifying speakers of quotations in narratives is an important task in literary analysis, with challenging scenarios including the out-of-domain inference for unseen speakers, and non-explicit cases where there are no speaker mentions in surrounding context.
A framework for mining lifestyle profiles through multi-dimensional and high-order mobility feature clustering
Human mobility demonstrates a high degree of regularity, which facilitates the discovery of lifestyle profiles.
Towards Open-World Product Attribute Mining: A Lightly-Supervised Approach
We present a new task setting for attribute mining on e-commerce products, serving as a practical solution to extract open-world attributes without extensive human intervention.
Few-Shot Character Understanding in Movies as an Assessment to Meta-Learning of Theory-of-Mind
Our dataset consists of ~1, 000 parsed movie scripts, each corresponding to a few-shot character understanding task that requires models to mimic humans' ability of fast digesting characters with a few starting scenes in a new movie.
Online Coreference Resolution for Dialogue Processing: Improving Mention-Linking on Real-Time Conversations
This paper suggests a direction of coreference resolution for online decoding on actively generated input such as dialogue, where the model accepts an utterance and its past context, then finds mentions in the current utterance as well as their referents, upon each dialogue turn.
Improving Downstream Task Performance by Treating Numbers as Entities
Numbers are essential components of text, like any other word tokens, from which natural language processing (NLP) models are built and deployed.
Modeling Task Interactions in Document-Level Joint Entity and Relation Extraction
We target on the document-level relation extraction in an end-to-end setting, where the model needs to jointly perform mention extraction, coreference resolution (COREF) and relation extraction (RE) at once, and gets evaluated in an entity-centric way.
Ranked #3 on
Joint Entity and Relation Extraction
on DocRED
coreference-resolution
Document-level Relation Extraction
+2
RescoreBERT: Discriminative Speech Recognition Rescoring with BERT
Specifically, training a bidirectional model like BERT on a discriminative objective such as minimum WER (MWER) has not been explored.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Zero-Shot Cross-Lingual Machine Reading Comprehension via Inter-sentence Dependency Graph
We target the task of cross-lingual Machine Reading Comprehension (MRC) in the direct zero-shot setting, by incorporating syntactic features from Universal Dependencies (UD), and the key features we use are the syntactic relations within each sentence.
ELIT: Emory Language and Information Toolkit
We introduce ELIT, the Emory Language and Information Toolkit, which is a comprehensive NLP framework providing transformer-based end-to-end models for core tasks with a special focus on memory efficiency while maintaining state-of-the-art accuracy and speed.
Adapted End-to-End Coreference Resolution System for Anaphoric Identities in Dialogues
We present an effective system adapted from the end-to-end neural coreference resolution model, targeting on the task of anaphora resolution in dialogues.
Boosting Cross-Lingual Transfer via Self-Learning with Uncertainty Estimation
Recent multilingual pre-trained language models have achieved remarkable zero-shot performance, where the model is only finetuned on one source language and directly evaluated on target languages.
Revealing the Myth of Higher-Order Inference in Coreference Resolution
We find that given a high-performing encoder such as SpanBERT, the impact of HOI is negative to marginal, providing a new perspective of HOI to this task.
Ranked #6 on
Coreference Resolution
on CoNLL 2012
Noise Pollution in Hospital Readmission Prediction: Long Document Classification with Reinforcement Learning
This paper presents a reinforcement learning approach to extract noise in long clinical documents for the task of readmission prediction after kidney transplant.
