Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network

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Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network ACL-IJCNLP 2015  Qiao Qian, Bo Tian, Minlie Huang, Yang Liu, Xuan Zhu and Xiaoyan Zhu

Technologie

Komachi Lab
M1 Ryosuke Miyazaki
2015/10/02
Learning Tag Embeddings and Tag-specific
Composition Functions in Recursive Neural Network
ACL-IJCNLP 2015 
Qiao Qian, Bo Tian, Minlie Huang, Yang Liu, Xuan Zhu and Xiaoyan Zhu
ACL 2015 reading group
※このスライド内の図は論文内のものです

Komachi Lab
RNN
✤ ２つの子ベクトルから親ベクトルを構成!
✤ 木構造に従って再帰的に文全体のベクトルを構成
2

Komachi Lab
Abstract
✤ VPを構成するcomposition functionと 
NPを構成するcomposition functionは本当に一緒？ 
タグごとに違うのでは？!
✤ 句構造タグも構成に利用しよう!
✤ Tag Guided RNNとTag Embedded RNN/RNTNを提案
3

Komachi Lab
Tag Guided RNN
✤ 親ノードのタグ毎にcomposition functionを変える!
✤ すべてのタグを区別すると 
頻度の低いタグに関するパラメータが学習しきれない
4
➡Top k 個とその他で 
k+1 の composition function

Komachi Lab
Tag Embedded RNN
✤ 親のタグが同じでも子のタグが違う場合 
(VP→V NP と VP→V Advは同じcomposition？)!
✤ 子ノードのタグ情報も使いたいけど 
Tag Guidedでやるとcomposition functionがk × k個も 
パラメータが多すぎる!
➡ タグをベクトルに埋め込んでスパースネス解消 
(1000回未満の出現頻度のタグは無視)
5

Komachi Lab
Tag Embedded RNTN
7
✤ RNTN(Recursive Neural Tensor Network)と同様!
✤ テンソルTを使って, 子ベクトルの相互作用を考慮する

Komachi Lab
Learning
✤ 今回はSentiment Analysis (分類問題)のタスクで学習
8
✤ 誤差関数はCross Entropy Error

Komachi Lab
Experiments Settings
✤ Sentiment Tree Bankを使用 
すべての部分木に極性ラベルがついている (5値) 
(train: 8544, dev: 1101, test:2210)!
✤ Stanford Parserを使って句構造タグを付与 
(Sentiment Tree BankもStanford Parserを使ってるが 
ver違いによる木構造の違いでtrain:74, dev:11,test11 の文を無視)
9

Komachi Lab
Experiments Settings
✤ 単語ベクトルはword2vecでpre-train(100,000 movie reviews)  
RNN:d=25, RNTN: d=20!
✤ その他のベクトルは[-0.01, 0.01]の一様分布からサンプル!
✤ 構成されたフレーズベクトルにはベクトルの正規化を施す
10

Komachi Lab
Experiments Settings
✤ ハイパーパラメータ 
batch size 30, momentum 0.9, L2 reg 0.0001, Learning rate 0.005
11
TG-RNNのタグの数k: 6 TE-RNNのタグの次元数 de: 8, TE-RNTN : 6

Komachi Lab
Results
12
✤ Fine-grainedではDRNN
の次によい!
✤ RNN → TG-RNN  
(+3.8)!
✤ RNN → TE-RNN  
(+4.8)!
✤ RNTN → TE-RNTN 
(+3.2)

Komachi Lab
Model Size
13
✤ DRNNよりも少ない!
✤ RNN, RNTNより少ないのは、次元dの設定が小さいから

Komachi Lab
Tag Vectors Analysis
✤ 目的関数にはタグの分類の気持ちは入っていないのに 
似たタグは似たベクトルになってる!
✤ 恐らく似たタグは似た役割を担うから
14

Komachi Lab
Conclusion
✤ 句構造タグを利用した 
新しいモデルTG-RNN, TE-RNN/RNTNを考案!
✤ 有用であるだけでなく便利である 
(比較的良いパフォーマンスと少ないパラメータ)
15

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Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network

1. Komachi Lab M1 Ryosuke Miyazaki 2015/10/02 Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network ACL-IJCNLP 2015  Qiao Qian, Bo Tian, Minlie Huang, Yang Liu, Xuan Zhu and Xiaoyan Zhu ACL 2015 reading group ※このスライド内の図は論文内のものです

2. Komachi Lab RNN ✤ ２つの子ベクトルから親ベクトルを構成! ✤ 木構造に従って再帰的に文全体のベクトルを構成 2

3. Komachi Lab Abstract ✤ VPを構成するcomposition functionと  NPを構成するcomposition functionは本当に一緒？  タグごとに違うのでは？! ✤ 句構造タグも構成に利用しよう! ✤ Tag Guided RNNとTag Embedded RNN/RNTNを提案 3

4. Komachi Lab Tag Guided RNN ✤ 親ノードのタグ毎にcomposition functionを変える! ✤ すべてのタグを区別すると  頻度の低いタグに関するパラメータが学習しきれない 4 ➡Top k 個とその他で  k+1 の composition function

5. Komachi Lab Tag Embedded RNN ✤ 親のタグが同じでも子のタグが違う場合  (VP→V NP と VP→V Advは同じcomposition？)! ✤ 子ノードのタグ情報も使いたいけど  Tag Guidedでやるとcomposition functionがk × k個も  パラメータが多すぎる! ➡ タグをベクトルに埋め込んでスパースネス解消  (1000回未満の出現頻度のタグは無視) 5

6. Komachi Lab Tag Embedded RNN 6

7. Komachi Lab Tag Embedded RNTN 7 ✤ RNTN(Recursive Neural Tensor Network)と同様! ✤ テンソルTを使って, 子ベクトルの相互作用を考慮する

8. Komachi Lab Learning ✤ 今回はSentiment Analysis (分類問題)のタスクで学習 8 ✤ 誤差関数はCross Entropy Error

9. Komachi Lab Experiments Settings ✤ Sentiment Tree Bankを使用  すべての部分木に極性ラベルがついている (5値)  (train: 8544, dev: 1101, test:2210)! ✤ Stanford Parserを使って句構造タグを付与  (Sentiment Tree BankもStanford Parserを使ってるが  ver違いによる木構造の違いでtrain:74, dev:11,test11 の文を無視) 9

10. Komachi Lab Experiments Settings ✤ 単語ベクトルはword2vecでpre-train(100,000 movie reviews)   RNN:d=25, RNTN: d=20! ✤ その他のベクトルは[-0.01, 0.01]の一様分布からサンプル! ✤ 構成されたフレーズベクトルにはベクトルの正規化を施す 10

11. Komachi Lab Experiments Settings ✤ ハイパーパラメータ  batch size 30, momentum 0.9, L2 reg 0.0001, Learning rate 0.005 11 TG-RNNのタグの数k: 6 TE-RNNのタグの次元数 de: 8, TE-RNTN : 6

12. Komachi Lab Results 12 ✤ Fine-grainedではDRNN の次によい! ✤ RNN → TG-RNN   (+3.8)! ✤ RNN → TE-RNN   (+4.8)! ✤ RNTN → TE-RNTN  (+3.2)

13. Komachi Lab Model Size 13 ✤ DRNNよりも少ない! ✤ RNN, RNTNより少ないのは、次元dの設定が小さいから

14. Komachi Lab Tag Vectors Analysis ✤ 目的関数にはタグの分類の気持ちは入っていないのに  似たタグは似たベクトルになってる! ✤ 恐らく似たタグは似た役割を担うから 14

15. Komachi Lab Conclusion ✤ 句構造タグを利用した  新しいモデルTG-RNN, TE-RNN/RNTNを考案! ✤ 有用であるだけでなく便利である  (比較的良いパフォーマンスと少ないパラメータ) 15

Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network

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Learning Tag Embeddings and Tag-specific Composition Functions in Recursive Neural Network