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Found 12 papers, 3 papers with code
Capturing Speaker Incorrectness: Speaker-Focused Post-Correction for Abstractive Dialogue Summarization
In this paper, we focus on improving the quality of the summary generated by neural abstractive dialogue summarization systems.
BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text
Automatic speech recognition (ASR) is arguably the most critical component of such systems, as errors in speech recognition propagate to the downstream components and drastically degrade the user experience.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Towards Reliable and Fluent Large Language Models: Incorporating Feedback Learning Loops in QA Systems
First, we build a dataset to train a critic model capable of evaluating the citation, correctness, and fluency of responses generated by LLMs in QA systems.
A Deep Dive into NFT Whales: A Longitudinal Study of the NFT Trading Ecosystem
NFT (Non-fungible Token) has drastically increased in its size, accounting for over \$16. 9B of total market capitalization.
Analysis of Utterance Embeddings and Clustering Methods Related to Intent Induction for Task-Oriented Dialogue
The focus of this work is to investigate unsupervised approaches to overcome quintessential challenges in designing task-oriented dialog schema: assigning intent labels to each dialog turn (intent clustering) and generating a set of intents based on the intent clustering methods (intent induction).
Language Chameleon: Transformation analysis between languages using Cross-lingual Post-training based on Pre-trained language models
This can be attributed to the fact that the amount of available training data in each language follows the power-law distribution, and most of the languages belong to the long tail of the distribution.
Multimodal Frame-Scoring Transformer for Video Summarization
Second, existing datasets for generic video summarization are relatively insufficient to train a caption generator used for extracting text information from a video and to train the multimodal feature extractors.
Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection
In this paper, we study the task of selecting the optimal response given a user and system utterance history in retrieval-based multi-turn dialog systems.
Ranked #5 on
Conversational Response Selection
on E-commerce
Variational Reward Estimator Bottleneck: Learning Robust Reward Estimator for Multi-Domain Task-Oriented Dialog
Despite its notable success in adversarial learning approaches to multi-domain task-oriented dialog system, training the dialog policy via adversarial inverse reinforcement learning often fails to balance the performance of the policy generator and reward estimator.
An Effective Domain Adaptive Post-Training Method for BERT in Response Selection
We focus on multi-turn response selection in a retrieval-based dialog system.
Rich Character-Level Information for Korean Morphological Analysis and Part-of-Speech Tagging
Due to the fact that Korean is a highly agglutinative, character-rich language, previous work on Korean morphological analysis typically employs the use of sub-character features known as graphemes or otherwise utilizes comprehensive prior linguistic knowledge (i. e., a dictionary of known morphological transformation forms, or actions).
Character-Level Feature Extraction with Densely Connected Networks
Generating character-level features is an important step for achieving good results in various natural language processing tasks.
