Seolhwa Lee


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BTS: Back TranScription for Speech-to-Text Post-Processor using Text-to-Speech-to-Text
Chanjun Park | Jaehyung Seo | Seolhwa Lee | Chanhee Lee | Hyeonseok Moon | Sugyeong Eo | Heuiseok Lim
Proceedings of the 8th Workshop on Asian Translation (WAT2021)

With the growing popularity of smart speakers, such as Amazon Alexa, speech is becoming one of the most important modes of human-computer interaction. Automatic speech recognition (ASR) is arguably the most critical component of such systems, as errors in speech recognition propagate to the downstream components and drastically degrade the user experience. A simple and effective way to improve the speech recognition accuracy is to apply automatic post-processor to the recognition result. However, training a post-processor requires parallel corpora created by human annotators, which are expensive and not scalable. To alleviate this problem, we propose Back TranScription (BTS), a denoising-based method that can create such corpora without human labor. Using a raw corpus, BTS corrupts the text using Text-to-Speech (TTS) and Speech-to-Text (STT) systems. Then, a post-processing model can be trained to reconstruct the original text given the corrupted input. Quantitative and qualitative evaluations show that a post-processor trained using our approach is highly effective in fixing non-trivial speech recognition errors such as mishandling foreign words. We present the generated parallel corpus and post-processing platform to make our results publicly available.

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Two Heads are Better than One? Verification of Ensemble Effect in Neural Machine Translation
Chanjun Park | Sungjin Park | Seolhwa Lee | Taesun Whang | Heuiseok Lim
Proceedings of the Second Workshop on Insights from Negative Results in NLP

In the field of natural language processing, ensembles are broadly known to be effective in improving performance. This paper analyzes how ensemble of neural machine translation (NMT) models affect performance improvement by designing various experimental setups (i.e., intra-, inter-ensemble, and non-convergence ensemble). To an in-depth examination, we analyze each ensemble method with respect to several aspects such as different attention models and vocab strategies. Experimental results show that ensembling is not always resulting in performance increases and give noteworthy negative findings.


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Collecting Verified COVID-19 Question Answer Pairs
Adam Poliak | Max Fleming | Cash Costello | Kenton Murray | Mahsa Yarmohammadi | Shivani Pandya | Darius Irani | Milind Agarwal | Udit Sharma | Shuo Sun | Nicola Ivanov | Lingxi Shang | Kaushik Srinivasan | Seolhwa Lee | Xu Han | Smisha Agarwal | João Sedoc
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

We release a dataset of over 2,100 COVID19 related Frequently asked Question-Answer pairs scraped from over 40 trusted websites. We include an additional 24, 000 questions pulled from online sources that have been aligned by experts with existing answered questions from our dataset. This paper describes our efforts in collecting the dataset and summarizes the resulting data. Our dataset is automatically updated daily and available at scraping-qas. So far, this data has been used to develop a chatbot providing users information about COVID-19. We encourage others to build analytics and tools upon this dataset as well.

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Using the Poly-encoder for a COVID-19 Question Answering System
Seolhwa Lee | João Sedoc
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

To combat misinformation regarding COVID- 19 during this unprecedented pandemic, we propose a conversational agent that answers questions related to COVID-19. We adapt the Poly-encoder (Humeau et al., 2020) model for informational retrieval from FAQs. We show that after fine-tuning, the Poly-encoder can achieve a higher F1 score. We make our code publicly available for other researchers to use.