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Found 74 papers, 25 papers with code
Analysis of Zero-Shot Crosslingual Learning between English and Korean for Named Entity Recognition
This paper presents a English-Korean parallel dataset that collects 381K news articles where 1, 400 of them, comprising 10K sentences, are manually labeled for crosslingual named entity recognition (NER).
Competence-Level Prediction and Resume \& Job Description Matching Using Context-Aware Transformer Models
Given this dataset, novel transformer-based classification models are developed for two tasks: the first task takes a resume and classifies it to a CRC level (T1), and the second task takes both a resume and a job description to apply and predicts if the application is suited to the job (T2).
Automatic Generation of Large-scale Multi-turn Dialogues from Reddit
Our best method is applied to posts from those 10 subreddits for the creation of a corpus comprising 10, 098 dialogues (3. 3M tokens), 570 of which are compared against dialogues in three other datasets, Blended Skill Talk, Daily Dialogue, and Topical Chat.
Automatic Enrichment of Abstract Meaning Representations
All code for this paper, including our automatic annotation tool, is made publicly available.
A Cognitive Approach to Annotating Causal Constructions in a Cross-Genre Corpus
We present a scheme for annotating causal language in various genres of text.
FantasyCoref: Coreference Resolution on Fantasy Literature Through Omniscient Writer’s Point of View
This paper presents a new corpus and annotation guideline for a novel coreference resolution task on fictional texts, and analyzes its unique characteristics.
UMR-Writer: A Web Application for Annotating Uniform Meaning Representations
We present UMR-Writer, a web-based application for annotating Uniform Meaning Representations (UMR), a graph-based, cross-linguistically applicable semantic representation developed recently to support the development of interpretable natural language applications that require deep semantic analysis of texts.
Unleashing the True Potential of Sequence-to-Sequence Models for Sequence Tagging and Structure Parsing
Sequence-to-Sequence (S2S) models have achieved remarkable success on various text generation tasks.
Don't Forget Your ABC's: Evaluating the State-of-the-Art in Chat-Oriented Dialogue Systems
There has been great recent advancement in human-computer chat.
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.
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
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.
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.
An Approach to Inference-Driven Dialogue Management within a Social Chatbot
In the third and final stage, our bot selects a small subset of predicates and translates them into an English response.
What Went Wrong? Explaining Overall Dialogue Quality through Utterance-Level Impacts
Improving user experience of a dialogue system often requires intensive developer effort to read conversation logs, run statistical analyses, and intuit the relative importance of system shortcomings.
Intensionalizing Abstract Meaning Representations: Non-Veridicality and Scope
We adopt a scope node from the literature and provide an explicit multidimensional semantics utilizing Cooper storage which allows us to derive the de re and de dicto scope readings as well as intermediate scope readings which prove difficult for accounts without a scope node.
StreamSide: A Fully-Customizable Open-Source Toolkit for Efficient Annotation of Meaning Representations
This demonstration paper presents StreamSide, an open-source toolkit for annotating multiple kinds of meaning representations.
The Stem Cell Hypothesis: Dilemma behind Multi-Task Learning with Transformer Encoders
Multi-task learning with transformer encoders (MTL) has emerged as a powerful technique to improve performance on closely-related tasks for both accuracy and efficiency while a question still remains whether or not it would perform as well on tasks that are distinct in nature.
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.
Levi Graph AMR Parser using Heterogeneous Attention
Coupled with biaffine decoders, transformers have been effectively adapted to text-to-graph transduction and achieved state-of-the-art performance on AMR parsing.
Ranked #14 on
AMR Parsing
on LDC2017T10
View Distillation with Unlabeled Data for Extracting Adverse Drug Effects from User-Generated Data
Then a classifier is trained on each view to label a set of unlabeled documents to be used as an initializer for a new classifier in the other view.
Evaluation of Unsupervised Entity and Event Salience Estimation
Salience Estimation aims to predict term importance in documents.
Analysis of Hierarchical Multi-Content Text Classification Model on B-SHARP Dataset for Early Detection of Alzheimer's Disease
This paper presents a new dataset, B-SHARP, that can be used to develop NLP models for the detection of Mild Cognitive Impairment (MCI) known as an early sign of Alzheimer{'}s disease.
Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models
This paper presents a comprehensive study on resume classification to reduce the time and labor needed to screen an overwhelming number of applications significantly, while improving the selection of suitable candidates.
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
Emora: An Inquisitive Social Chatbot Who Cares For You
Inspired by studies on the overwhelming presence of experience-sharing in human-human conversations, Emora, the social chatbot developed by Emory University, aims to bring such experience-focused interaction to the current field of conversational AI.
XD at SemEval-2020 Task 12: Ensemble Approach to Offensive Language Identification in Social Media Using Transformer Encoders
This paper presents six document classification models using the latest transformer encoders and a high-performing ensemble model for a task of offensive language identification in social media.
Adaptation of Multilingual Transformer Encoder for Robust Enhanced Universal Dependency Parsing
Our results show that models using the multilingual encoder outperform ones using the language specific encoders for most languages.
Emora STDM: A Versatile Framework for Innovative Dialogue System Development
This demo paper presents Emora STDM (State Transition Dialogue Manager), a dialogue system development framework that provides novel workflows for rapid prototyping of chat-based dialogue managers as well as collaborative development of complex interactions.
Towards Unified Dialogue System Evaluation: A Comprehensive Analysis of Current Evaluation Protocols
As conversational AI-based dialogue management has increasingly become a trending topic, the need for a standardized and reliable evaluation procedure grows even more pressing.
Analysis of the Penn Korean Universal Dependency Treebank (PKT-UD): Manual Revision to Build Robust Parsing Model in Korean
In this paper, we first open on important issues regarding the Penn Korean Universal Treebank (PKT-UD) and address these issues by revising the entire corpus manually with the aim of producing cleaner UD annotations that are more faithful to Korean grammar.
Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media
We present a transformer-based sarcasm detection model that accounts for the context from the entire conversation thread for more robust predictions.
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.
Transformers to Learn Hierarchical Contexts in Multiparty Dialogue for Span-based Question Answering
We introduce a novel approach to transformers that learns hierarchical representations in multiparty dialogue.
Ranked #3 on
Question Answering
on FriendsQA
Automatic Text-based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings
Previous works related to automatic personality recognition focus on using traditional classification models with linguistic features.
Incremental Sense Weight Training for the Interpretation of Contextualized Word Embeddings
We hypothesize that not all dimensions are equally important for downstream tasks so that our algorithm can detect unessential dimensions and discard them without hurting the performance.
Design and Challenges of Cloze-Style Reading Comprehension Tasks on Multiparty Dialogue
According to our analysis, replacing the random data split with a chronological data split reduces test accuracy on previous single-variable passage completion task from 72\% to 34\%, that leaves much more room to improve.
FriendsQA: Open-Domain Question Answering on TV Show Transcripts
This paper presents FriendsQA, a challenging question answering dataset that contains 1, 222 dialogues and 10, 610 open-domain questions, to tackle machine comprehension on everyday conversations.
Establishing Strong Baselines for the New Decade: Sequence Tagging, Syntactic and Semantic Parsing with BERT
This paper presents new state-of-the-art models for three tasks, part-of-speech tagging, syntactic parsing, and semantic parsing, using the cutting-edge contextualized embedding framework known as BERT.
Meta-Semantic Representation for Early Detection of Alzheimer's Disease
This paper presents a new task-oriented meaning representation called meta-semantics, that is designed to detect patients with early symptoms of Alzheimer{'}s disease by analyzing their language beyond a syntactic or semantic level.
The Pupil Has Become the Master: Teacher-Student Model-Based Word Embedding Distillation with Ensemble Learning
Recent advances in deep learning have facilitated the demand of neural models for real applications.
Ranked #2 on
Sentiment Analysis
on MPQA
Multimodal Ensemble Approach to Incorporate Various Types of Clinical Notes for Predicting Readmission
One of the modalities in EHRs, clinical notes, has not been fully explored for these tasks due to its unstructured and inexplicable nature.
Coordinate Structures in Universal Dependencies for Head-final Languages
This paper discusses the representation of coordinate structures in the Universal Dependencies framework for two head-final languages, Japanese and Korean.
They Exist! Introducing Plural Mentions to Coreference Resolution and Entity Linking
To the best of our knowledge, this is the first time that plural mentions are thoroughly analyzed for these two resolution tasks.
Challenging Reading Comprehension on Daily Conversation: Passage Completion on Multiparty Dialog
This paper presents a new corpus and a robust deep learning architecture for a task in reading comprehension, passage completion, on multiparty dialog.
SemEval 2018 Task 4: Character Identification on Multiparty Dialogues
Character identification is a task of entity linking that finds the global entity of each personal mention in multiparty dialogue.
Analysis of Wikipedia-based Corpora for Question Answering
This paper gives comprehensive analyses of corpora based on Wikipedia for several tasks in question answering.
Improving Document Clustering by Removing Unnatural Language
We view this problem as an information extraction task and build a multiclass classification model identifying unnatural language components into four categories.
Document Layout Analysis
Optical Character Recognition (OCR)
Classification of Radiology Reports Using Neural Attention Models
The electronic health record (EHR) contains a large amount of multi-dimensional and unstructured clinical data of significant operational and research value.
Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks
While there have been significant advances in detecting emotions from speech and image recognition, emotion detection on text is still under-explored and remained as an active research field.
ISS-MULT: Intelligent Sample Selection for Multi-Task Learning in Question Answering
In addition, using ISS-MULT could finely improve the MULT method for question answering tasks, and these improvements prove more significant in the answer triggering task.
Robust Coreference Resolution and Entity Linking on Dialogues: Character Identification on TV Show Transcripts
This paper presents a novel approach to character identification, that is an entity linking task that maps mentions to characters in dialogues from TV show transcripts.
Cross-genre Document Retrieval: Matching between Conversational and Formal Writings
This paper challenges a cross-genre document retrieval task, where the queries are in formal writing and the target documents are in conversational writing.
Improving Document Clustering by Eliminating Unnatural Language
We view this problem as an information extraction task and build a multiclass classification model identifying unnatural language components into four categories.
Lexicon Integrated CNN Models with Attention for Sentiment Analysis
With the advent of word embeddings, lexicons are no longer fully utilized for sentiment analysis although they still provide important features in the traditional setting.
SelQA: A New Benchmark for Selection-based Question Answering
This paper presents a new selection-based question answering dataset, SelQA.
Multi-Field Structural Decomposition for Question Answering
This paper presents a precursory yet novel approach to the question answering task using structural decomposition.
Targetable Named Entity Recognition in Social Media
We present a novel approach for recognizing what we call targetable named entities; that is, named entities in a targeted set (e. g, movies, books, TV shows).
Preparing Korean Data for the Shared Task on Parsing Morphologically Rich Languages
This document gives a brief description of Korean data prepared for the SPMRL 2013 shared task.
Empty Argument Insertion in the Hindi PropBank
This paper examines both linguistic behavior and practical implication of empty argument insertion in the Hindi PropBank.
