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Found 39 papers, 17 papers with code
Entity-based De-noising Modeling for Controllable Dialogue Summarization
Although fine-tuning pre-trained backbones produces fluent and grammatically-correct text in various language generation tasks, factual consistency in abstractive summarization remains challenging.
Velocidapter: Task-oriented Dialogue Comprehension Modeling Pairing Synthetic Text Generation with Domain Adaptation
We introduce a synthetic dialogue generation framework, Velocidapter, which addresses the corpus availability problem for dialogue comprehension.
Singlish Message Paraphrasing: A Joint Task of Creole Translation and Text Normalization
In this work, we introduce a joint paraphrasing task of creole translation and text normalization of Singlish messages, which can shed light on how to process other language varieties and dialects.
In2Core: Leveraging Influence Functions for Coreset Selection in Instruction Finetuning of Large Language Models
By applying our algorithm to instruction fine-tuning data of LLMs, we can achieve similar performance with just 50% of the training data.
AudioBench: A Universal Benchmark for Audio Large Language Models
We introduce AudioBench, a universal benchmark designed to evaluate Audio Large Language Models (AudioLLMs).
Ranked #1 on
Audio Scene Understanding
on Clotho-AQA
Decompose and Aggregate: A Step-by-Step Interpretable Evaluation Framework
The acceleration of Large Language Models (LLMs) research has opened up new possibilities for evaluating generated texts.
CRAFT: Extracting and Tuning Cultural Instructions from the Wild
Large language models (LLMs) have rapidly evolved as the foundation of various natural language processing (NLP) applications.
CrossIn: An Efficient Instruction Tuning Approach for Cross-Lingual Knowledge Alignment
This performance discrepancy mainly stems from the imbalanced distribution of training data across languages during pre-training and instruction tuning stages.
Resilience of Large Language Models for Noisy Instructions
As the rapidly advancing domain of natural language processing (NLP), large language models (LLMs) have emerged as powerful tools for interpreting human commands and generating text across various tasks.
Automatic Speech Recognition
Optical Character Recognition
+3
Personality-aware Student Simulation for Conversational Intelligent Tutoring Systems
Intelligent Tutoring Systems (ITSs) can provide personalized and self-paced learning experience.
Scaffolding Language Learning via Multi-modal Tutoring Systems with Pedagogical Instructions
Intelligent tutoring systems (ITSs) that imitate human tutors and aim to provide immediate and customized instructions or feedback to learners have shown their effectiveness in education.
Learning Planning-based Reasoning by Trajectories Collection and Process Reward Synthesizing
Large Language Models (LLMs) have demonstrated significant potential in handling complex reasoning tasks through step-by-step rationale generation.
Picking the Underused Heads: A Network Pruning Perspective of Attention Head Selection for Fusing Dialogue Coreference Information
In this work, we investigate the attention head selection and manipulation strategy for feature injection from a network pruning perspective, and conduct a case study on dialogue summarization.
Multi-label and Multi-target Sampling of Machine Annotation for Computational Stance Detection
Data collection from manual labeling provides domain-specific and task-aligned supervision for data-driven approaches, and a critical mass of well-annotated resources is required to achieve reasonable performance in natural language processing tasks.
CoAnnotating: Uncertainty-Guided Work Allocation between Human and Large Language Models for Data Annotation
Annotated data plays a critical role in Natural Language Processing (NLP) in training models and evaluating their performance.
Instructive Dialogue Summarization with Query Aggregations
With the advancement of instruction-finetuned language models, we introduce instruction-tuning to dialogues to expand the capability set of dialogue summarization models.
Ranked #1 on
Text Summarization
on DialogSum
SeaEval for Multilingual Foundation Models: From Cross-Lingual Alignment to Cultural Reasoning
We present SeaEval, a benchmark for multilingual foundation models.
Guiding Computational Stance Detection with Expanded Stance Triangle Framework
Stance detection determines whether the author of a piece of text is in favor of, against, or neutral towards a specified target, and can be used to gain valuable insights into social media.
Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity
Transfer learning provides the possibility to solve this problem, but there are too many features in natural images that are not related to the target domain.
Exploring Self-supervised Logic-enhanced Training for Large Language Models
Existing efforts to improve logical reasoning ability of language models have predominantly relied on supervised fine-tuning, hindering generalization to new domains and/or tasks.
Domain-specific Language Pre-training for Dialogue Comprehension on Clinical Inquiry-Answering Conversations
There is growing interest in the automated extraction of relevant information from clinical dialogues.
Learning from Bootstrapping and Stepwise Reinforcement Reward: A Semi-Supervised Framework for Text Style Transfer
To take advantage of both supervised and unsupervised paradigms and tackle the challenges, in this work, we propose a semi-supervised framework for text style transfer.
DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsing
While previous work significantly improves the performance of RST discourse parsing, they are not readily applicable to practical use cases: (1) EDU segmentation is not integrated into most existing tree parsing frameworks, thus it is not straightforward to apply such models on newly-coming data.
Ranked #2 on
End-to-End RST Parsing
on RST-DT
(using extra training data)
Improving Multi-Party Dialogue Discourse Parsing via Domain Integration
While multi-party conversations are often less structured than monologues and documents, they are implicitly organized by semantic level correlations across the interactive turns, and dialogue discourse analysis can be applied to predict the dependency structure and relations between the elementary discourse units, and provide feature-rich structural information for downstream tasks.
Ranked #4 on
Discourse Parsing
on STAC
Controllable Neural Dialogue Summarization with Personal Named Entity Planning
The conditional sequences are modulated to decide what types of information or what perspective to focus on when forming summaries to tackle the under-constrained problem in summarization tasks.
Dynamic Sliding Window for Meeting Summarization
In this work, we first analyze the linguistic characteristics of meeting transcripts on a representative corpus, and find that the sentences comprising the summary correlate with the meeting agenda.
Coreference-Aware Dialogue Summarization
Summarizing conversations via neural approaches has been gaining research traction lately, yet it is still challenging to obtain practical solutions.
N-Shot Learning for Augmenting Task-Oriented Dialogue State Tracking
Augmentation of task-oriented dialogues has followed standard methods used for plain-text such as back-translation, word-level manipulation, and paraphrasing despite its richly annotated structure.
An End-to-End Document-Level Neural Discourse Parser Exploiting Multi-Granularity Representations
Document-level discourse parsing, in accordance with the Rhetorical Structure Theory (RST), remains notoriously challenging.
Multilingual Neural RST Discourse Parsing
Text discourse parsing plays an important role in understanding information flow and argumentative structure in natural language.
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks
While neural approaches have achieved significant improvement in machine comprehension tasks, models often work as a black-box, resulting in lower interpretability, which requires special attention in domains such as healthcare or education.
Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization
In this paper, we propose a neural framework that can flexibly control summary generation by introducing a set of sub-aspect functions (i. e. importance, diversity, position).
Retinal Vessel Segmentation based on Fully Convolutional Networks
The morphological attributes of retinal vessels, such as length, width, tortuosity and branching pattern and angles, play an important role in diagnosis, screening, treatment, and evaluation of various cardiovascular and ophthalmologic diseases such as diabetes, hypertension and arteriosclerosis.
Bayesian Recurrent Framework for Missing Data Imputation and Prediction with Clinical Time Series
Real-world clinical time series data sets exhibit a high prevalence of missing values.
Exploiting Discourse-Level Segmentation for Extractive Summarization
We investigate how the sub-sentential segmentation improves extractive summarization performance when content selection is modeled through two basic neural network architectures and a deep bi-directional transformer.
Topic-aware Pointer-Generator Networks for Summarizing Spoken Conversations
Such linguistic characteristics of dialogue topics make sentence-level extractive summarization approaches used in spoken documents ill-suited for summarizing conversations.
Reading Turn by Turn: Hierarchical Attention Architecture for Spoken Dialogue Comprehension
Comprehending multi-turn spoken conversations is an emerging research area, presenting challenges different from reading comprehension of passages due to the interactive nature of information exchange from at least two speakers.
Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels
The reliable segmentation of retinal vasculature can provide the means to diagnose and monitor the progression of a variety of diseases affecting the blood vessel network, including diabetes and hypertension.
Fast Prototyping a Dialogue Comprehension System for Nurse-Patient Conversations on Symptom Monitoring
Data for human-human spoken dialogues for research and development are currently very limited in quantity, variety, and sources; such data are even scarcer in healthcare.
