Papers by Genady Beryozkin
A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy (P19-1)
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| Challenge: | Named-entity recognition (NER) has seen significant progress with the application of Neural Networks to the task. |
| Approach: | They propose to use a given tag hierarchy to jointly learn a neural network that shares its tagging layer among all tag-sets. |
| Outcome: | The proposed model outperforms models that combine independent models and multitasking approaches in a domain adaptation for named-entity recognition task. |
Audio De-identification - a New Entity Recognition Task (N19-2)
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Ido Cohn, Itay Laish, Genady Beryozkin, Gang Li, Izhak Shafran, Idan Szpektor, Tzvika Hartman, Avinatan Hassidim, Yossi Matias
| Challenge: | Named Entity Recognition (NER) is an important step in de-identification (de-ID) of medical records, many of which are recorded conversations between a patient and a doctor. |
| Approach: | They propose to use Named Entity Recognition (NER) to detect audio spans with entity mentions in medical records and then use it to evaluate the results. |
| Outcome: | The proposed pipeline is based on a large labeled segment of the Switchboard and Fisher audio datasets and compares it with a benchmark. |
RED-ACE: Robust Error Detection for ASR using Confidence Embeddings (2022.emnlp-main)
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| Challenge: | ASR Error Detection (AED) models post-process the output of Automatic Speech Recognition systems, in order to detect transcription errors. |
| Approach: | They propose to use ASR model's word-level confidence scores to combine ASR models with transcribed text to improve AED performance. |
| Outcome: | The proposed models combine the confidence scores and transcribed text into a contextualized representation. |
KoBE: Knowledge-Based Machine Translation Evaluation (2020.findings-emnlp)
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| Challenge: | Existing methods for machine translation evaluation do not require reference translations. |
| Approach: | They propose a method for machine translation evaluation which does not require reference translations. |
| Outcome: | The proposed method achieves highest correlation with human judgements on 9 out of 18 language pairs from the WMT19 benchmark for evaluation without references. |