Search Results for author:
Found 31 papers, 13 papers with code
Leveraging the Interplay Between Syntactic and Acoustic Cues for Optimizing Korean TTS Pause Formation
Contemporary neural speech synthesis models have indeed demonstrated remarkable proficiency in synthetic speech generation as they have attained a level of quality comparable to that of human-produced speech.
Evalverse: Unified and Accessible Library for Large Language Model Evaluation
This paper introduces Evalverse, a novel library that streamlines the evaluation of Large Language Models (LLMs) by unifying disparate evaluation tools into a single, user-friendly framework.
Explainable Multi-hop Question Generation: An End-to-End Approach without Intermediate Question Labeling
We also prove that our model logically and incrementally increases the complexity of questions, and the generated multi-hop questions are also beneficial for training question answering models.
sDPO: Don't Use Your Data All at Once
As development of large language models (LLM) progresses, aligning them with human preferences has become increasingly important.
Denoising Table-Text Retrieval for Open-Domain Question Answering
Previous studies in table-text open-domain question answering have two common challenges: firstly, their retrievers can be affected by false-positive labels in training datasets; secondly, they may struggle to provide appropriate evidence for questions that require reasoning across the table.
Autoregressive Score Generation for Multi-trait Essay Scoring
Recently, encoder-only pre-trained models such as BERT have been successfully applied in automated essay scoring (AES) to predict a single overall score.
Word-Level ASR Quality Estimation for Efficient Corpus Sampling and Post-Editing through Analyzing Attentions of a Reference-Free Metric
The findings suggest that NoRefER is not merely a tool for error detection but also a comprehensive framework for enhancing ASR systems' transparency, efficiency, and effectiveness.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
SOLAR 10.7B: Scaling Large Language Models with Simple yet Effective Depth Up-Scaling
We introduce SOLAR 10. 7B, a large language model (LLM) with 10. 7 billion parameters, demonstrating superior performance in various natural language processing (NLP) tasks.
Optimizing Two-Pass Cross-Lingual Transfer Learning: Phoneme Recognition and Phoneme to Grapheme Translation
This research contributes to the advancements of two-pass ASR systems in low-resource languages, offering the potential for improved cross-lingual transfer learning.
Exploring the Viability of Synthetic Audio Data for Audio-Based Dialogue State Tracking
We address this by investigating synthetic audio data for audio-based DST.
Score-balanced Loss for Multi-aspect Pronunciation Assessment
With rapid technological growth, automatic pronunciation assessment has transitioned toward systems that evaluate pronunciation in various aspects, such as fluency and stress.
Prompt- and Trait Relation-aware Cross-prompt Essay Trait Scoring
Thus, predicting various trait scores of unseen-prompt essays (called cross-prompt essay trait scoring) is a remaining challenge of AES.
Bring More Attention to Syntactic Symmetry for Automatic Postediting of High-Quality Machine Translations
Automatic postediting (APE) is an automated process to refine a given machine translation (MT).
DORIC : Domain Robust Fine-Tuning for Open Intent Clustering through Dependency Parsing
We present our work on Track 2 in the Dialog System Technology Challenges 11 (DSTC11).
Self-Training with Purpose Preserving Augmentation Improves Few-shot Generative Dialogue State Tracking
In dialogue state tracking (DST), labeling the dataset involves considerable human labor.
Hierarchical Pronunciation Assessment with Multi-Aspect Attention
In this paper, we propose a Hierarchical Pronunciation Assessment with Multi-aspect Attention (HiPAMA) model, which hierarchically represents the granularity levels to directly capture their linguistic structures and introduces multi-aspect attention that reflects associations across aspects at the same level to create more connotative representations.
Multi-Type Conversational Question-Answer Generation with Closed-ended and Unanswerable Questions
Conversational question answering (CQA) facilitates an incremental and interactive understanding of a given context, but building a CQA system is difficult for many domains due to the problem of data scarcity.
When and Why is Unsupervised Neural Machine Translation Useless?
This paper studies the practicality of the current state-of-the-art unsupervised methods in neural machine translation (NMT).
When and Why is Document-level Context Useful in Neural Machine Translation?
Document-level context has received lots of attention for compensating neural machine translation (NMT) of isolated sentences.
Pivot-based Transfer Learning for Neural Machine Translation between Non-English Languages
We present effective pre-training strategies for neural machine translation (NMT) using parallel corpora involving a pivot language, i. e., source-pivot and pivot-target, leading to a significant improvement in source-target translation.
The RWTH Aachen University Machine Translation Systems for WMT 2019
For the De-En task, none of the tested methods gave a significant improvement over last years winning system and we end up with the same performance, resulting in 39. 6{\%} BLEU on newstest2019.
Generalizing Back-Translation in Neural Machine Translation
Back-translation - data augmentation by translating target monolingual data - is a crucial component in modern neural machine translation (NMT).
Learning Bilingual Sentence Embeddings via Autoencoding and Computing Similarities with a Multilayer Perceptron
We propose a novel model architecture and training algorithm to learn bilingual sentence embeddings from a combination of parallel and monolingual data.
Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies
Transfer learning or multilingual model is essential for low-resource neural machine translation (NMT), but the applicability is limited to cognate languages by sharing their vocabularies.
Cross-Lingual Transfer
Low-Resource Neural Machine Translation
+3
A Comparative Study on Vocabulary Reduction for Phrase Table Smoothing
This work systematically analyzes the smoothing effect of vocabulary reduction for phrase translation models.
Unsupervised Training for Large Vocabulary Translation Using Sparse Lexicon and Word Classes
We address for the first time unsupervised training for a translation task with hundreds of thousands of vocabulary words.
Improving Unsupervised Word-by-Word Translation with Language Model and Denoising Autoencoder
Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences.
The RWTH Aachen University Filtering System for the WMT 2018 Parallel Corpus Filtering Task
We use several rule-based, heuristic methods to preselect sentence pairs.
The RWTH Aachen University Supervised Machine Translation Systems for WMT 2018
In total we improve by 6. 8{\%} BLEU over our last year{'}s submission and by 4. 8{\%} BLEU over the winning system of the 2017 German→English task.
The RWTH Aachen University English-German and German-English Unsupervised Neural Machine Translation Systems for WMT 2018
This paper describes the unsupervised neural machine translation (NMT) systems of the RWTH Aachen University developed for the English ↔ German news translation task of the \textit{EMNLP 2018 Third Conference on Machine Translation} (WMT 2018).
