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Found 37 papers, 12 papers with code
Capture Human Disagreement Distributions by Calibrated Networks for Natural Language Inference
Natural Language Inference (NLI) datasets contain examples with highly ambiguous labels due to its subjectivity.
Make the Blind Translator See The World: A Novel Transfer Learning Solution for Multimodal Machine Translation
Based on large-scale pretrained networks and the liability to be easily overfitting with limited labelled training data of multimodal translation (MMT) is a critical issue in MMT.
HW-TSC’s Participation at WMT 2020 Automatic Post Editing Shared Task
The paper presents the submission by HW-TSC in the WMT 2020 Automatic Post Editing Shared Task.
HI-CMLM: Improve CMLM with Hybrid Decoder Input
Mask-predict CMLM (Ghazvininejad et al., 2019) has achieved stunning performance among non-autoregressive NMT models, but we find that the mechanism of predicting all of the target words only depending on the hidden state of [MASK] is not effective and efficient in initial iterations of refinement, resulting in ungrammatical repetitions and slow convergence.
HW-TSC’s Participation at WMT 2020 Quality Estimation Shared Task
This paper presents our work in the WMT 2020 Word and Sentence-Level Post-Editing Quality Estimation (QE) Shared Task.
How Length Prediction Influence the Performance of Non-Autoregressive Translation?
Length prediction is a special task in a series of NAT models where target length has to be determined before generation.
HW-TSC’s Participation in the WMT 2021 Efficiency Shared Task
This paper presents the submission of Huawei Translation Services Center (HW-TSC) to WMT 2021 Efficiency Shared Task.
HW-TSC’s Participation at WMT 2021 Quality Estimation Shared Task
This paper presents our work in WMT 2021 Quality Estimation (QE) Shared Task.
The HW-TSC’s Offline Speech Translation System for IWSLT 2022 Evaluation
For machine translation part, we pretrained three translation models on WMT21 dataset and fine-tuned them on in-domain corpora.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
The HW-TSC’s Speech to Speech Translation System for IWSLT 2022 Evaluation
The paper presents the HW-TSC’s pipeline and results of Offline Speech to Speech Translation for IWSLT 2022.
HW-TSC at SemEval-2022 Task 7: Ensemble Model Based on Pretrained Models for Identifying Plausible Clarifications
This paper describes the system for the identifying Plausible Clarifications of Implicit and Underspecified Phrases.
HwTscSU’s Submissions on WAT 2022 Shared Task
In this paper we describe our submission to the shared tasks of the 9th Workshop on Asian Translation (WAT 2022) on NICT–SAP under the team name ”HwTscSU”.
M-Ped: Multi-Prompt Ensemble Decoding for Large Language Models
Given a unique input $X$, we submit $n$ variations of prompts with $X$ to LLMs in batch mode to decode and derive probability distributions.
Adapting Large Language Models to Log Analysis with Interpretable Domain Knowledge
The increasing complexity of computer systems necessitates innovative approaches to fault and error management, going beyond traditional manual log analysis.
LogLM: From Task-based to Instruction-based Automated Log Analysis
Automatic log analysis is essential for the efficient Operation and Maintenance (O&M) of software systems, providing critical insights into system behaviors.
What Do You Want? User-centric Prompt Generation for Text-to-image Synthesis via Multi-turn Guidance
To achieve this, we mined 15 essential dimensions for high-quality prompts from advanced users and curated a multi-turn dataset.
LogEval: A Comprehensive Benchmark Suite for Large Language Models In Log Analysis
These findings provide insights into the strengths and weaknesses of LLMs in multilingual environments and the effectiveness of different prompt strategies.
From Handcrafted Features to LLMs: A Brief Survey for Machine Translation Quality Estimation
Machine Translation Quality Estimation (MTQE) is the task of estimating the quality of machine-translated text in real time without the need for reference translations, which is of great importance for the development of MT.
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimation
Given the significant resource allocation required for training and evaluating models, it is advantageous to have an efficient method for selecting high-quality IT data.
DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware Translators
In this paper, we propose an alternative adaptation approach, named Decoding-enhanced Multi-phase Prompt Tuning (DeMPT), to make LLMs discriminately model and utilize the inter- and intra-sentence context and more effectively adapt LLMs to context-aware NMT.
Using Large Language Model for End-to-End Chinese ASR and NER
In this work, we connect the Whisper encoder with ChatGLM3 and provide in-depth comparisons of these two approaches using Chinese automatic speech recognition (ASR) and name entity recognition (NER) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
UCorrect: An Unsupervised Framework for Automatic Speech Recognition Error Correction
Previous works usually adopt end-to-end models and has strong dependency on Pseudo Paired Data and Original Paired Data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
CoachLM: Automatic Instruction Revisions Improve the Data Quality in LLM Instruction Tuning
Instruction tuning is crucial for enabling Language Learning Models (LLMs) in responding to human instructions.
Unify word-level and span-level tasks: NJUNLP's Participation for the WMT2023 Quality Estimation Shared Task
We generate pseudo MQM data using parallel data from the WMT translation task.
A Multitask Training Approach to Enhance Whisper with Contextual Biasing and Open-Vocabulary Keyword Spotting
The recognition of rare named entities, such as personal names and terminologies, is challenging for automatic speech recognition (ASR) systems, especially when they are not frequently observed in the training data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Translate Meanings, Not Just Words: IdiomKB's Role in Optimizing Idiomatic Translation with Language Models
To translate well, machine translation (MT) systems and general-purposed language models (LMs) need a deep understanding of both source and target languages and cultures.
Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies
LogPrompt employs large language models (LLMs) to perform online log analysis tasks via a suite of advanced prompt strategies tailored for log tasks, which enhances LLMs' performance by up to 380. 7% compared with simple prompts.
Collective Human Opinions in Semantic Textual Similarity
Despite the subjective nature of semantic textual similarity (STS) and pervasive disagreements in STS annotation, existing benchmarks have used averaged human ratings as the gold standard.
Knowledge-Prompted Estimator: A Novel Approach to Explainable Machine Translation Assessment
Cross-lingual Machine Translation (MT) quality estimation plays a crucial role in evaluating translation performance.
P-Transformer: Towards Better Document-to-Document Neural Machine Translation
To alleviate this problem, we propose a position-aware Transformer (P-Transformer) to enhance both the absolute and relative position information in both self-attention and cross-attention.
Neighbors Are Not Strangers: Improving Non-Autoregressive Translation under Low-Frequency Lexical Constraints
To this end, we propose a plug-in algorithm for this line of work, i. e., Aligned Constrained Training (ACT), which alleviates this problem by familiarizing the model with the source-side context of the constraints.
Diformer: Directional Transformer for Neural Machine Translation
In this paper, we aim to close the gap by preserving the original objective of AR and NAR under a unified framework.
Self-Distillation Mixup Training for Non-autoregressive Neural Machine Translation
An effective training strategy to improve the performance of AT models is Self-Distillation Mixup (SDM) Training, which pre-trains a model on raw data, generates distilled data by the pre-trained model itself and finally re-trains a model on the combination of raw data and distilled data.
Joint-training on Symbiosis Networks for Deep Nueral Machine Translation models
Deep encoders have been proven to be effective in improving neural machine translation (NMT) systems, but it reaches the upper bound of translation quality when the number of encoder layers exceeds 18.
UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks
UniLog: Deploy One Model and Specialize it for All Log Analysis Tasks
The HW-TSC's Offline Speech Translation Systems for IWSLT 2021 Evaluation
This paper describes our work in participation of the IWSLT-2021 offline speech translation task.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
