Ebru Arisoy


2024

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Dealing with Data Scarcity in Spoken Question Answering
Merve Ünlü Menevşe | Yusufcan Manav | Ebru Arisoy | Arzucan Özgür
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)

This paper focuses on dealing with data scarcity in spoken question answering (QA) using automatic question-answer generation and a carefully selected fine-tuning strategy that leverages limited annotated data (paragraphs and question-answer pairs). Spoken QA is a challenging task due to using spoken documents, i.e., erroneous automatic speech recognition (ASR) transcriptions, and the scarcity of spoken QA data. We propose a framework for utilizing limited annotated data effectively to improve spoken QA performance. To deal with data scarcity, we train a question-answer generation model with annotated data and then produce large amounts of question-answer pairs from unannotated data (paragraphs). Our experiments demonstrate that incorporating limited annotated data and the automatically generated data through a carefully selected fine-tuning strategy leads to 5.5% relative F1 gain over the model trained only with annotated data. Moreover, the proposed framework is also effective in high ASR errors.

2022

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A Framework for Automatic Generation of Spoken Question-Answering Data
Merve Ünlü Menevşe | Yusufcan Manav | Ebru Arisoy | Arzucan Özgür
Findings of the Association for Computational Linguistics: EMNLP 2022

This paper describes a framework to automatically generate a spoken question answering (QA) dataset. The framework consists of a question generation (QG) module to generate questions automatically from given text documents, a text-to-speech (TTS) module to convert the text documents into spoken form and an automatic speech recognition (ASR) module to transcribe the spoken content. The final dataset contains question-answer pairs for both the reference text and ASR transcriptions as well as the audio files corresponding to each reference text. For QG and ASR systems we used pre-trained multilingual encoder-decoder transformer models and fine-tuned these models using a limited amount of manually generated QA data and TTS-based speech data, respectively. As a proof of concept, we investigated the proposed framework for Turkish and generated the Turkish Question Answering (TurQuAse) dataset using Wikipedia articles. Manual evaluation of the automatically generated question- answer pairs and QA performance evaluation with state of-the-art models on TurQuAse show that the proposed framework is efficient for automatically generating spoken QA datasets. To the best of our knowledge, TurQuAse is the first publicly available spoken question answering dataset for Turkish. The proposed framework can be easily extended to other languages where a limited amount of QA data is available.

2012

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Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT
Bhuvana Ramabhadran | Sanjeev Khudanpur | Ebru Arisoy
Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT

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Deep Neural Network Language Models
Ebru Arisoy | Tara N. Sainath | Brian Kingsbury | Bhuvana Ramabhadran
Proceedings of the NAACL-HLT 2012 Workshop: Will We Ever Really Replace the N-gram Model? On the Future of Language Modeling for HLT

2008

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Proceedings of the ACL-08: HLT Student Research Workshop
Ebru Arisoy | Keisuke Inoue | Wolfgang Maier
Proceedings of the ACL-08: HLT Student Research Workshop

2007

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Analysis of Morph-Based Speech Recognition and the Modeling of Out-of-Vocabulary Words Across Languages
Mathias Creutz | Teemu Hirsimäki | Mikko Kurimo | Antti Puurula | Janne Pylkkönen | Vesa Siivola | Matti Varjokallio | Ebru Arisoy | Murat Saraçlar | Andreas Stolcke
Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference

2006

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Unlimited vocabulary speech recognition for agglutinative languages
Mikko Kurimo | Antti Puurula | Ebru Arisoy | Vesa Siivola | Teemu Hirsimäki | Janne Pylkkönen | Tanel Alumäe | Murat Saraclar
Proceedings of the Human Language Technology Conference of the NAACL, Main Conference