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Found 65 papers, 9 papers with code
FINDINGS OF THE IWSLT 2021 EVALUATION CAMPAIGN
The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2021) featured this year four shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Multilingual speech translation, (iv) Low-resource speech translation.
NAIST English-to-Japanese Simultaneous Translation System for IWSLT 2021 Simultaneous Text-to-text Task
This paper describes NAIST’s system for the English-to-Japanese Simultaneous Text-to-text Translation Task in IWSLT 2021 Evaluation Campaign.
NAIST Simultaneous Speech-to-Text Translation System for IWSLT 2022
This paper describes NAIST’s simultaneous speech translation systems developed for IWSLT 2022 Evaluation Campaign.
Simultaneous Neural Machine Translation with Prefix Alignment
Simultaneous translation is a task that requires starting translation before the speaker has finished speaking, so we face a trade-off between latency and accuracy.
Pseudo Ambiguous and Clarifying Questions Based on Sentence Structures Toward Clarifying Question Answering System
Ambiguous questions are generated by eliminating a part of a sentence considering the sentence structure.
Multilingual Machine Translation Evaluation Metrics Fine-tuned on Pseudo-Negative Examples for WMT 2021 Metrics Task
This paper describes our submission to the WMT2021 shared metrics task.
Large-Scale English-Japanese Simultaneous Interpretation Corpus: Construction and Analyses with Sentence-Aligned Data
This paper describes the construction of a new large-scale English-Japanese Simultaneous Interpretation (SI) corpus and presents the results of its analysis.
Using Spoken Word Posterior Features in Neural Machine Translation
In this paper, a neural sequence-to-sequence ASR is used as feature processing that is trained to produce word posterior features given spoken utterances.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
On Knowledge Distillation for Translating Erroneous Speech Transcriptions
Recent studies argue that knowledge distillation is promising for speech translation (ST) using end-to-end models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Multi-paraphrase Augmentation to Leverage Neural Caption Translation
In this paper, we investigate and utilize neural paraphrasing to improve translation quality in neural MT (NMT), which has not yet been much explored.
Is This Translation Error Critical?: Classification-Based Human and Automatic Machine Translation Evaluation Focusing on Critical Errors
Our classification-based approach focuses on such errors using several error type labels, for practical machine translation evaluation in an age of neural machine translation.
Multi-Source Cross-Lingual Constituency Parsing
Pretrained multilingual language models have become a key part of cross-lingual transfer for many natural language processing tasks, even those without bilingual information.
Named Entity-Factored Transformer for Proper Noun Translation
The proposed method applies the Named Entity (NE) fea-ture vector to Factored Transformer for accurate proper noun translation.
Findings of the IWSLT 2022 Evaluation Campaign
The evaluation campaign of the 19th International Conference on Spoken Language Translation featured eight shared tasks: (i) Simultaneous speech translation, (ii) Offline speech translation, (iii) Speech to speech translation, (iv) Low-resource speech translation, (v) Multilingual speech translation, (vi) Dialect speech translation, (vii) Formality control for speech translation, (viii) Isometric speech translation.
Overview of the IWSLT 2017 Evaluation Campaign
The IWSLT 2017 evaluation campaign has organised three tasks.
TransLLaMa: LLM-based Simultaneous Translation System
Decoder-only large language models (LLMs) have recently demonstrated impressive capabilities in text generation and reasoning.
Average Token Delay: A Duration-aware Latency Metric for Simultaneous Translation
In this work, we propose a novel latency evaluation metric for simultaneous translation called \emph{Average Token Delay} (ATD) that focuses on the duration of partial translations.
Tagged End-to-End Simultaneous Speech Translation Training using Simultaneous Interpretation Data
In this paper, we propose an effective way to train a SimulST model using mixed data of SI and offline.
Improving Speech Translation Accuracy and Time Efficiency with Fine-tuned wav2vec 2.0-based Speech Segmentation
In this study, we extended SHAS to improve ST translation accuracy and efficiency by splitting speech into shorter segments that correspond to sentences.
NAIST-SIC-Aligned: an Aligned English-Japanese Simultaneous Interpretation Corpus
Research has been limited due to the lack of a large-scale training corpus.
Modeling Multiple User Interests using Hierarchical Knowledge for Conversational Recommender System
A conversational recommender system (CRS) is a practical application for item recommendation through natural language conversation.
Evaluating the Robustness of Discrete Prompts
To address this question, we conduct a systematic study of the robustness of discrete prompts by applying carefully designed perturbations into an application using AutoPrompt and then measure their performance in two Natural Language Inference (NLI) datasets.
Average Token Delay: A Latency Metric for Simultaneous Translation
In this work, we propose a novel latency evaluation metric called Average Token Delay (ATD) that focuses on the end timings of partial translations in simultaneous translation.
E2E Refined Dataset
Although the well-known MR-to-text E2E dataset has been used by many researchers, its MR-text pairs include many deletion/insertion/substitution errors.
Subspace Representations for Soft Set Operations and Sentence Similarities
In the field of natural language processing (NLP), continuous vector representations are crucial for capturing the semantic meanings of individual words.
Speech Segmentation Optimization using Segmented Bilingual Speech Corpus for End-to-end Speech Translation
We also propose a hybrid method that combines VAD and the above speech segmentation method.
Simultaneous Neural Machine Translation with Constituent Label Prediction
Simultaneous translation is a task in which translation begins before the speaker has finished speaking, so it is important to decide when to start the translation process.
Using Perturbed Length-aware Positional Encoding for Non-autoregressive Neural Machine Translation
Non-autoregressive neural machine translation (NAT) usually employs sequence-level knowledge distillation using autoregressive neural machine translation (AT) as its teacher model.
ARTA: Collection and Classification of Ambiguous Requests and Thoughtful Actions
In order to train the classification model on such training data, we applied the positive/unlabeled (PU) learning method, which assumes that only a part of the data is labeled with positive examples.
Improving Spoken Language Understanding by Wisdom of Crowds
Spoken language understanding (SLU), which converts user requests in natural language to machine-interpretable expressions, is becoming an essential task.
Incorporating Noisy Length Constraints into Transformer with Length-aware Positional Encodings
Since length constraints with exact target sentence lengths degrade translation performance, we add random noise within a certain window size to the length constraints in the PE during the training.
Simultaneous Speech-to-Speech Translation System with Neural Incremental ASR, MT, and TTS
This paper presents a newly developed, simultaneous neural speech-to-speech translation system and its evaluation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Image Captioning with Visual Object Representations Grounded in the Textual Modality
We present our work in progress exploring the possibilities of a shared embedding space between textual and visual modality.
Reflection-based Word Attribute Transfer
For transferring king into queen in this analogy-based manner, we subtract a difference vector man - woman based on the knowledge that king is male.
Automatic Machine Translation Evaluation using Source Language Inputs and Cross-lingual Language Model
Our experiments show that our proposed method using Cross-lingual Language Model (XLM) trained with a translation language modeling (TLM) objective achieves a higher correlation with human judgments than a baseline method that uses only hypothesis and reference sentences.
NAIST's Machine Translation Systems for IWSLT 2020 Conversational Speech Translation Task
This paper describes NAIST{'}s NMT system submitted to the IWSLT 2020 conversational speech translation task.
Simultaneous Neural Machine Translation using Connectionist Temporal Classification
Simultaneous machine translation is a variant of machine translation that starts the translation process before the end of an input.
Findings of the Third Workshop on Neural Generation and Translation
This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019).
Conversational Response Re-ranking Based on Event Causality and Role Factored Tensor Event Embedding
We propose a novel method for selecting coherent and diverse responses for a given dialogue context.
Another Diversity-Promoting Objective Function for Neural Dialogue Generation
Although generation-based dialogue systems have been widely researched, the response generations by most existing systems have very low diversities.
Multi-Source Neural Machine Translation with Data Augmentation
By using information from these multiple sources, these systems achieve large gains in accuracy.
Training Neural Machine Translation using Word Embedding-based Loss
The proposed loss function encourages an NMT decoder to generate words close to their references in the embedding space; this helps the decoder to choose similar acceptable words when the actual best candidates are not included in the vocabulary due to its size limitation.
Multi-Source Neural Machine Translation with Missing Data
This study focuses on the use of incomplete multilingual corpora in multi-encoder NMT and mixture of NMT experts and examines a very simple implementation where missing source translations are replaced by a special symbol <NULL>.
A Simple and Strong Baseline: NAIST-NICT Neural Machine Translation System for WAT2017 English-Japanese Translation Task
This paper describes the details about the NAIST-NICT machine translation system for WAT2017 English-Japanese Scientific Paper Translation Task.
An Empirical Study of Mini-Batch Creation Strategies for Neural Machine Translation
Training of neural machine translation (NMT) models usually uses mini-batches for efficiency purposes.
Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings
One of the most important problems in machine translation (MT) evaluation is to evaluate the similarity between translation hypotheses with different surface forms from the reference, especially at the segment level.
Reading Comprehension using Entity-based Memory Network
This paper introduces a novel neural network model for question answering, the \emph{entity-based memory network}.
Exploring Text Links for Coherent Multi-Document Summarization
Existing methods focus on the extraction of key information, but often neglect coherence.
Chinese-to-Japanese Patent Machine Translation based on Syntactic Pre-ordering for WAT 2016
This paper presents our Chinese-to-Japanese patent machine translation system for WAT 2016 (Group ID: ntt) that uses syntactic pre-ordering over Chinese dependency structures.
Neural Reordering Model Considering Phrase Translation and Word Alignment for Phrase-based Translation
This paper presents an improved lexicalized reordering model for phrase-based statistical machine translation using a deep neural network.
