Papers with OpenSubtitles
Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation (L18-1)
Copied to clipboard
| Challenge: | Recent work in spoken language translation (SLT) has attempted to build end-to-end speech-totext translation without using source language transcription during learning or decoding. |
| Approach: | They propose to augment an existing (monolingual) corpus: LibriSpeech. |
| Outcome: | The proposed corpus is derived from read audiobooks from the LibriVox project and has been carefully segmented and aligned. |
An Empirical Study on the Overlapping Problem of Open-Domain Dialogue Datasets (2022.lrec-1)
Copied to clipboard
| Challenge: | Existing benchmark datasets for open-domain dialogue generation are advancing the field . overlapping between training and test sets can cause fake performance . |
| Approach: | They analyze dailyDialog and OpenSubtitles to find out how overlapping can be exploited to obtain fake state-of-the-art performance. |
| Outcome: | The proposed datasets are cleaned and set up for future research. |
Non-Parametric Adaptation for Neural Machine Translation (N19-1)
Copied to clipboard
| Challenge: | Neural Networks trained with gradient descent are susceptible to catastrophic forgetting due to parameter shift during the training process. |
| Approach: | They propose a semi-parametric approach that relies on local phrase level similarities to retrieve neighboring phrases that are useful for translation even when overall sentence similarity is low. |
| Outcome: | The proposed approach performs well on a heterogeneous dataset with WMT, IWSLT, JRC-Acquis and OpenSubtitles. |
Hierarchical Pre-training for Sequence Labelling in Spoken Dialog (2020.findings-emnlp)
Copied to clipboard
| Challenge: | Sequence labelling tasks like Dialog Act and Emotion/Sentiment identification are a key component of spoken dialog systems. |
| Approach: | They propose a new approach to learn generic representations adapted to spoken dialog using a hierarchical encoder based on transformer architectures. |
| Outcome: | The proposed model is model-agnostic and contains 10 datasets of various sizes. |
Classifying Sluice Occurrences in Dialogue (L18-1)
Copied to clipboard
| Challenge: | Ellipsis is an important challenge for natural language processing systems, says a new paper . previous work on ellipsis focused on news data, but sluicing presents a challenge for dialogue systems . |
| Approach: | They describe a corpus of 4100 sluice occurrences from the NYTimes Gigaword corpus . they build a classifier model to automatically classify slujce . |
| Outcome: | The proposed corpus contains 4100 sluice occurrences, with an accuracy of 67% . the work will support empirical research into slujcing in dialogue systems . |
Code-switched inspired losses for spoken dialog representations (2021.emnlp-main)
Copied to clipboard
| Challenge: | We introduce new pretraining losses tailored to learn generic multilingual spoken dialogue representations . goal is to expose model to code-switched language . |
| Approach: | They propose to build a pretraining corpus of multilingual conversations in five different languages from OpenSubtitles. |
| Outcome: | The proposed models perform better in monolingual and multilingual settings. |
NeuTral Rewriter: A Rule-Based and Neural Approach to Automatic Rewriting into Gender Neutral Alternatives (2021.emnlp-main)
Copied to clipboard
| Challenge: | Recent years have seen an increasing need for gender-neutral and inclusive language. |
| Approach: | They propose a rule-based and a neural approach to gender-neutral rewriting for English . they use manually curated synthetic and natural data to train a rewriter . |
| Outcome: | The proposed approach improves on the rule-based approach with word error rates below 0.18% on synthetic, in-domain and out-domain test sets. |
Is Child-Directed Speech Effective Training Data for Language Models? (2024.emnlp-main)
Copied to clipboard
| Challenge: | High-performing language models are typically trained on hundreds of billions of words, but human learners use language fluently after far less training data. |
| Approach: | They train GPT-2 and RoBERTa models on 29M words of English child-directed speech and a new matched, synthetic dataset. |
| Outcome: | The proposed models show that child language input is not valuable for training language models. |