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[輪読会]Multilingual Image Description with Neural Sequence Models

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Deep Learning JP
Deep Learning JP

2016/9/9 Deep Learning JP: http://deeplearning.jp/seminar-2/

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輪読 : MULTILINGUAL IMAGE DESCRIPTION WITH NEURAL SEQUENCE MODELS h6p://arxiv.org/abs/1510.04709
書誌情報 •  Title: MULTILINGUAL IMAGE DESCRIPTION WITH NEURAL SEQUENCE MODELS •  Author: Desmond Ellio6, Stella Frank, Eva Hasler •  AffiliaTon: University of Amsterdam, Cambridge •  URL: h6p://arxiv.org/abs/1510.04709 •  ICLR’16 不採録 •  概要 : MulTlingual Image DescripTon –  ある画像に対して言語Aのキャプションを生成するとき，言語Bの情報も使う
MoTvaTon •  関連研究 : キャプション生成 –  入力画像に対して，その画像の説明文を生成する –  画像とその画像に対するキャプションのデータを利用して学習 •  この論文の主題 –  ある画像に対して，言語Bでキャプションを生成するとき，言語Aでのキャプションを生か すことができるか? –  MulTlingual Image DescripTon
Approach •  MulTlingual mulTmodal language model •  ターゲットのキャプションを生成するのに，以下の2つの特徴量を使う –  monolingual source-language image descripTon model –  visual features from an object recogniTon model
モデル : Recurrent Language Model (LM) •  RNNである単語を入力したとき，次の単語を予測するように訓練 –  入力 w_i (あるステップiにおいて)
モデル : MulTmodal Language Model (MLM) •  画像の情報をLMに組み込む –  画像特徴量で条件付ければ良い –  一つの方法 : h_0 の計算をする際に画像特徴量を入れる •  各タイムステップで画像特徴量を入れると， overfidng するという研究報告が複数ある
モデル : TranslaTon Model (Source-LM → Target-LM) •  画像の情報をの代わりに，source language modelで条件付
モデル : MulTlingual MulTmodal Model (Source-MLM → Target-MLM) •  画像とsource language model両方使う
NMT (Neural Machine TranslaTon) モデルとの違い •  NMT –  (翻訳元言語, 翻訳先言語) のペアで学習 •  このモデル –  データセットの扱いがより柔軟 (言語のペアを用意しなくても良い) –  source-language modelとtarget-language modelは別々のものでも良い •  e.g. sequense-to-sequense, encoder-decode, …
実験 : 使用したデータ •  データ : IAPR-TC12 –  画像数 : 20000 –  英語のキャプションと，対応するドイツ語訳 –  17,665枚を訓練に利用 –  英語 : 272,172 トークン (語彙数 1763) (出現頻度3以下は除去) –  ドイツ語: 223,147 トークン (語彙数2374) –  画像特徴量はVGG-16を利用して抽出
実験 : 結果 •  Baselin MLM : Monolingual Language Model (MulTmodal Language Model without source language features) LM → LM : no image MLM よりも LM→LMの方が良い ドイツ語のキャプション生成結果 (全体的に英語より難しい) sourceに画像特徴量を入れた方 (sourceでMLMを使う) が効果的
t-SNEによる隠れ層初期値の可視化 (左) MLM (右) De MLM → En MLM ドイツ語のキャプション生成結果 (全体的に英語より難しい) sourceに画像特徴量を入れた方 (sourceでMLMを使う) が効果的
source language modelを加えたことによるスコアの変動 元々スコアが高かったものは，source language modelを入れると スコアが下がる傾向にある
まとめ •  画像キャプショニングをする際に，別の言語のキャプションを利用する方 法の提案 •  マルチモーダルな翻訳の一つ •  単純に画像と言語を組み合わせるだけだと，なかなかスコアが上がらな い •  (ドイツ語のキャプション生成の実施) –  英語より難しい
ACL’16でのMulTmodal Machine TranslaTon •  h6p://www.statmt.org/wmt16/mulTmodal-task.html •  今回の著者らがオーガナイザー •  データセット: flickr30k –  英語のキャプションと，それに対応するドイツ語訳 •  タスク 1.  MulTmodal Machine TranslaTon 2.  Mulilingual Image DescripTon •  結論を言うと，あんまり良いのは無かった
Result 結果 : タスク1 (下線がベースライン; 灰色は外部データの利用)
結果 : タスク2 (下線がベースライン; 灰色は外部データの利用)
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