WonKee Lee


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Transformer-based Screenplay Summarization Using Augmented Learning Representation with Dialogue Information
Myungji Lee | Hongseok Kwon | Jaehun Shin | WonKee Lee | Baikjin Jung | Jong-Hyeok Lee
Proceedings of the Third Workshop on Narrative Understanding

Screenplay summarization is the task of extracting informative scenes from a screenplay. The screenplay contains turning point (TP) events that change the story direction and thus define the story structure decisively. Accordingly, this task can be defined as the TP identification task. We suggest using dialogue information, one attribute of screenplays, motivated by previous work that discovered that TPs have a relation with dialogues appearing in screenplays. To teach a model this characteristic, we add a dialogue feature to the input embedding. Moreover, in an attempt to improve the model architecture of previous studies, we replace LSTM with Transformer. We observed that the model can better identify TPs in a screenplay by using dialogue information and that a model adopting Transformer outperforms LSTM-based models.


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POSTECH-ETRI’s Submission to the WMT2020 APE Shared Task : Automatic Post-Editing with Cross-lingual Language ModelPOSTECH-ETRI’s Submission to the WMT2020 APE Shared Task: Automatic Post-Editing with Cross-lingual Language Model
Jihyung Lee | WonKee Lee | Jaehun Shin | Baikjin Jung | Young-Kil Kim | Jong-Hyeok Lee
Proceedings of the Fifth Conference on Machine Translation

This paper describes POSTECH-ETRI’s submission to WMT2020 for the shared task on automatic post-editing (APE) for 2 language pairs : English-German (En-De) and English-Chinese (En-Zh). We propose APE systems based on a cross-lingual language model, which jointly adopts translation language modeling (TLM) and masked language modeling (MLM) training objectives in the pre-training stage ; the APE models then utilize jointly learned language representations between the source language and the target language. In addition, we created 19 million new sythetic triplets as additional training data for our final ensemble model. According to experimental results on the WMT2020 APE development data set, our models showed an improvement over the baseline by TER of -3.58 and a BLEU score of +5.3 for the En-De subtask ; and TER of -5.29 and a BLEU score of +7.32 for the En-Zh subtask.