Papers with TED talks
Analysing Coreference in Transformer Outputs (D19-65)
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| Challenge: | Using a transformer architecture, we study coreference phenomena in three neural machine translation systems. |
| Approach: | They analyse coreference phenomena in three neural machine translation systems . they manually annotate (the possibly incorrect) coreference chains in the outputs . |
| Outcome: | The proposed model shows stronger translationese effects in machine translated outputs than in human translations. |
More Parameters? No Thanks! (2021.findings-acl)
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| Challenge: | Using network pruning, we find that there are large redundancies in MNMT models. |
| Approach: | They propose a method to prune and retrain redundant parameters of an MNMT model to improve bilingual representations while retaining multilinguality. |
| Outcome: | The proposed method improves bilingual representations while retaining multilinguality. |
Extreme Adaptation for Personalized Neural Machine Translation (P18-2)
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| Challenge: | Existing models that capture speaker-related variations do not include explicit information about the speaker. |
| Approach: | They propose a method that adapts the bias of the output softmax to each particular user . they propose to model speaker-related variations as an additional bias vector in the softmax layer . |
| Outcome: | The proposed technique improves translation accuracy and better reflection of speaker traits in target text. |
The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error (2022.findings-acl)
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| Challenge: | Discourse analysis is very low on texts outside of the training distribution’s coverage, diminishing the practical utility of existing models. |
| Approach: | They propose to use a distribution shift statistic to estimate the error-gap of a discourse model and to use it to estimate it. |
| Outcome: | The proposed model can be estimated via distribution shift but does not correlate with change in the observed error of a classifier (i.e. error-gap). |
Towards Modeling the Style of Translators in Neural Machine Translation (2021.naacl-main)
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| Challenge: | a key ingredient of neural machine translation is the use of large datasets with different but consistent translation styles . however, the models do not capture the variety of translators' styles from the data . a recent study shows that style-augmented models can capture the style variations of translator . |
| Approach: | They propose to augment a neural machine translation model with translator information . they use TED talk datasets to model and control translator-related stylistic variations . |
| Outcome: | The proposed models capture the style variations of translators and generate translations with different styles on new data. |
Role-specific Language Models for Processing Recorded Neuropsychological Exams (N18-2)
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| Challenge: | Neuropsychological examinations are an important screening tool for the presence of cognitive conditions such as Alzheimer's, Parkinson's and spinal-cord injuries. |
| Approach: | They propose to use audio recordings to determine the cognitive health of 92 subjects from audio that was diarized using an automatic speech recognition system trained on TED talks and on structured language used by testers and subjects. |
| Outcome: | The proposed method can determine the cognitive health of 92 subjects from audio that was diarized using an automatic speech recognition system trained on TED talks and on the structured language used by testers and subjects. |
Shallow Discourse Annotation for Chinese TED Talks (2020.lrec-1)
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| Challenge: | Existing methods to annotate text with discourse properties are limited to newspaper articles and are not available in Chinese. |
| Approach: | They propose to annotate TED talks with Chinese-related properties using the Penn Discourse TreeBank annotation style . they propose to use planned monologues instead of written text to annnotate Chinese-specific properties. |
| Outcome: | The proposed method is able to achieve reliable results in Chinese spoken monologues, and is based on the Penn Discourse TreeBank annotation style. |
The Correlation Between Emotion in Text and Speech Segments is Limited: A Cross-Modal Study (2026.findings-eacl)
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| Challenge: | a recent study has shown that text-to-speech systems can capture human-like emotion, but they lack the ability to predict emotion in speech. |
| Approach: | They propose to use 8 large language models for identifying emotion in text and 2 audio models for emotion in speech to investigate the correlation between emotion and speech. |
| Outcome: | The proposed models perform well on emotion recognition from situational text and audiobooks, but show weak correlation for Valence only. |
TED-CDB: A Large-Scale Chinese Discourse Relation Dataset on TED Talks (2020.emnlp-main)
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| Challenge: | TED-CDB dataset is a unique corpus of spoken discourse in Chinese . TED is based on the concept that discourse relations are grounded in an identifiable set of discourse connectives or Altlex expressions. |
| Approach: | They have created a dataset that annotates TED talks in Chinese . they propose to adapt the dataset to Chinese news text to improve its performance . |
| Outcome: | The TED-CDB dataset can improve the performance of systems for languages other than Chinese . it is adapted to features that are not present in English and can extract discourse semantic features . |
Language Tags Matter for Zero-Shot Neural Machine Translation (2021.findings-acl)
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| Challenge: | Existing studies on multilingual machine translation have ignored the importance of LTs. |
| Approach: | They propose to use language tag (LT) strategies to indicate translation directions in MNMT to enhance consistency and alleviate off-target issues in zero-shot directions. |
| Outcome: | The proposed model could translate between unsupervised languages and achieve a +8 BLEU score difference over other LT strategies in translation tasks. |
Monolingual Adapters for Zero-Shot Neural Machine Translation (2020.emnlp-main)
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| Challenge: | Existing adapter layers are more parameter-efficient and provide better performance than bilingual ones. |
| Approach: | They propose to use monolingual adapter layers instead of bilingual ones to compose them and generalize to unseen language pairs. |
| Outcome: | The proposed adapter layer formalism achieves a median improvement of +2.77 BLEU points over a 20-language multilingual Transformer baseline trained on TED talks. |
DOC-RAG: ASR Language Model Personalization with Domain-Distributed Co-occurrence Retrieval Augmentation (2024.lrec-main)
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| Challenge: | Extensive experiments on three user-specific speech-to-text tasks show that DOC-RAG significantly outperforms strong baselines with an 8-15% improvement in terms of perplexity and a 4-7% reduction in terms in terms . of Word Error Rates. |
| Approach: | They propose a domain-distributed co-occurrence augmentation approach to improve automatic speech recognition of rare word patterns in unseen domains by using n-gram co-existence distributions. |
| Outcome: | Experiments on three user-specific speech-to-text tasks show that DOC-RAG outperforms baselines with an 8-15% improvement in terms of perplexity and a 4-7% reduction in terms in terms . of Word Error Rates. |
Adapting to Non-Centered Languages for Zero-shot Multilingual Translation (2022.coling-1)
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| Challenge: | Existing studies attributed zero-shot translation to domination of central language, e.g. English, but we supplement this viewpoint with the strict dependence of non-centered languages. |
| Approach: | They propose a language-specific modeling method that adapts to non-centered languages to counteract the instability of zero-shot translation. |
| Outcome: | The proposed method performs better than baselines in centered data conditions and can easily fit non-centered data. |
PersonaLM: Language Model Personalization via Domain-distributed Span Aggregated K-Nearest N-gram Retrieval Augmentation (2023.findings-emnlp)
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| Challenge: | Existing language modeling tools for automatic speech recognition (ASR) are difficult to personalize. |
| Approach: | They propose a domain-distributed Span-Aggregated K-nearest N-gram retrieval augmentation to improve language modeling for automatic speech recognition (ASR) personalization. |
| Outcome: | The proposed model outperforms baselines on Wikitext-103, UserLibri, and ASAP datasets with a 10-16% improvement in perplexity and a 5-8% reduction in word error rates. |