POSTECH Submission on Duolingo Shared Task

2020-07-01 · WS 2020 7 · Junsu Park, Hong-Seok Kwon, Jong-Hyeok Lee

In this paper, we propose a transfer learning based simultaneous translation model by extending BART. We pre-trained BART with Korean Wikipedia and a Korean news dataset, and fine-tuned with an additional web-crawled parallel corpus and the 2020 Duolingo official training dataset. In our experiments on the 2020 Duolingo test dataset, our submission achieves 0.312 in weighted macro F1 score, and ranks second among the submitted En-Ko systems.

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Transfer Learning Translation

Methods 이 논문이 사용한 방법론

Linear Layer A Linear Layer is a projection $\mathbf{XW + b}$.

Softmax The Softmax output function transforms a previous layer's output into a vector of probabilities. It is commonly used for multiclass classification. Given an input vector $x$…

BPE Byte Pair Encoding, or BPE, is a subword segmentation algorithm that encodes rare and unknown words as sequences of subword units. The intuition is that various word…

Dense Connections Dense Connections, or Fully Connected Connections, are a type of layer in a deep neural network that use a linear operation where every input is connected to every output…

Layer Normalization Unlike batch normalization, Layer Normalization directly estimates the normalization statistics from the summed inputs…

Attention 설명 없음

Residual Connection 설명 없음

Multi-Head Attention 설명 없음

The JHU Submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education

2020-07-01 · WS 2020 7 · Huda Khayrallah, Jacob Bremerman, Arya D. McCarthy, Kenton Murray 외

This paper presents the Johns Hopkins University submission to the 2020 Duolingo Shared Task on Simultaneous Translation and Paraphrase for Language Education (STAPLE). We participated in all five language tasks, placing…

Machine Translation Translation

POSTECH-ETRI’s Submission to the WMT2020 APE Shared Task: Automatic Post-Editing with Cross-lingual Language Model

2020-11-01 · WMT (EMNLP) 2020 11 · Jihyung Lee, WonKee Lee, Jaehun Shin, Baikjin Jung 외

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 cr…

Automatic Post-Editing Language Modeling Language Modelling Masked Language Modeling+1

Multi-encoder Transformer Network for Automatic Post-Editing

2018-10-01 · WS 2018 10 · Jaehun Shin, Jong-Hyeok Lee

This paper describes the POSTECH{'}s submission to the WMT 2018 shared task on Automatic Post-Editing (APE). We propose a new neural end-to-end post-editing model based on the transformer network. We modified the encoder…

Automatic Post-Editing Decoder Machine Translation NMT+1

Exploring Model Consensus to Generate Translation Paraphrases

2020-07-01 · WS 2020 7 · Zhenhao Li, Marina Fomicheva, Lucia Specia

This paper describes our submission to the 2020 Duolingo Shared Task on Simultaneous Translation And Paraphrase for Language Education (STAPLE). This task focuses on improving the ability of neural MT systems to generate…

Diversity Machine Translation Mixture-of-Experts model+1

Grotoco@SLAM: Second Language Acquisition Modeling with Simple Features, Learners and Task-wise Models

2018-06-01 · WS 2018 6 · Sigrid Klerke, H{\'e}ctor Mart{\'\i}nez Alonso, Barbara Plank

We present our submission to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We focus on evaluating a range of features for the task, including user-derived measures, while examining how far…

Language Acquisition