Skip to content

Latest commit

 

History

History
51 lines (42 loc) · 1.33 KB

README.md

File metadata and controls

51 lines (42 loc) · 1.33 KB

Naver AI Hackathon 2018

Naver AI Hackathon 2018 task was to predict movie ratings and the similarities between two questions.
I participated as a team Deeppangyo.

Summary of approach

  • Movie rating prediction: Ranked 9th (over 200 teams, MSE: 2.86229)
  • Two question similarity prediction: Ranked 8th (1st round) / 11th (2nd round) (ACC: 0.960798/0.960328)

Features

Movie rating prediction

  • Fully dockerized environments
  • Bidirectional LSTM (Phoneme + Char + Word)
  • L2 regularization
  • Word CNN
  • Self Attention (but not used)

Two question similarity prediction

  • Siamese network using Manhattan distance
  • Contrastive loss
  • Convolution + Bidirectional GRU
  • Self Attention
  • GAP/GMP
  • Word CNN (but not used)

Usage

Movie rating prediction on NSML

Login with nsml, and run commands as follows:

$ cd movie-review
$ nsml run -d movie_final -e main.py -a "--epochs 10 --batch 1000 --strmaxlen 117 --embedding 64 --dropout 0.3"

Two question similarity prediction on NSML

Login with nsml, and run commands as follows:

$ cd kin
$ nsml run -d kin_final -e main.py -a "--epochs 200 --batch 2000 --strmaxlen 250 --embedding 128 --valrate 0.3"

Requirements

  • python 3
  • keras
  • torch
  • numpy
  • matplotlib
  • tensorflow
  • pandas
  • sklearn