Skip to content

Latest commit

 

History

History
145 lines (127 loc) · 8.88 KB

experiments.md

File metadata and controls

145 lines (127 loc) · 8.88 KB
Raw

SentencePiece Experiments

Experiments 1 (subword vs word-based model)

Experimental settings

  • Segmentation algorithms:

    • SentencePiece: SentencePiece with a language-model based segmentation. (--model_type=unigram)
    • SentencePiece(BPE): SentencePiece with Byte Pair Encoding. [Sennrich et al.]] (--model_type=bpe)
    • Moses: Moses tokenizer for English.
    • KyTea: KyTea for Japanese.
    • MeCab: MeCab for Japanese.
    • neologd: MeCab with neologd for Japanese.
    • (Moses/KyTea)+SentencePiece: Apply SentencePiece (Unigram) to pre-tokenized sentences. We have several variants with different tokenizers., e.g., (Moses/MeCab)+SentencePiece, (MeCab/Moses)+SentencePiece.
    • char*: Segments sentence by characters.

  • Data sets:

  • NMT parameters: (Google’s Neural Machine Translation System is applied for all experiments.)

    • Dropout prob: 0.2
    • num nodes: 512
    • num lstms: 6
    • Decoder parameters (α and β) are optimized with development data.

  • Evaluation metrics:

    • Case-sensitive BLEU on detokenized text with NIST scorer and KyTea segmenter. Used in-house rule-based detokenizer for Moses/KyTea/MeCab/neologd.

Results (BLEU scores)

English to Japanese

Setting vocab size BLEU(dev) BLEU(test) src #tokens/sent. trg #tokens/sent.
SentencePiece 4k (shared) 0.2857 0.2940 43.7478 29.6998
SentencePiece 8k (shared) 0.2785 0.2955 30.9734 25.0540
SentencePiece 16k (shared) 0.2664 0.2862 27.1827 21.5326
SentencePiece 32k (shared) 0.2641 0.2849 25.0592 19.0840
SentencePiece(BPE) 8k (shared) 0.2767 0.2947 31.7693 25.4331
(Moses/KyTea)+SentencePiece 8k (shared) 0.2900 0.2985 31.2719 29.9854
(Moses/MeCab)+SentencePiece 8k (shared) 0.2817 0.2950 31.4743 28.9537
(Moses/neologd)+SentencePiece 8k (shared) 0.2824 0.3062 31.2985 28.8645
Moses/Kytea 80k/80k 0.2576 0.2824 21.2513 23.2161
Moses/MeCab 80k/80k 0.2455 0.2780 21.2513 21.2033
Moses/neologd 80k/80k 0.2157 0.2378 21.2513 18.4768
Moses/SentencePiece 80k/8k 0.2475 0.2742 21.2513 22.9383
SentencePiece/KyTea 8k/80k 0.2778 0.2918 27.0429 23.2161
SentencePiece/MeCab 8k/80k 0.2673 0.2919 27.0429 21.2033
SentencePiece/neolgod 8k80k 0.2280 0.2494 27.0429 18.4768
Char 3k (shared) 0.2509 0.2679 109.8662 33.6963

Japanese to English

Setting vocab size BLEU(dev) BLEU(test) src #tokens/sent. trg #tokens/sent.
SentencePiece 4k (shared) 0.1970 0.2179 29.6998 43.7478
SentencePiece 8k (shared) 0.1966 0.2162 25.0540 30.9734
SentencePiece 16k (shared) 0.1996 0.2160 21.5326 27.1827
SentencePiece 32k (shared) 0.1949 0.2159 19.0840 25.0592
SentencePiece(BPE) 8k (shared) 0.1977 0.2173 25.4331 31.7693
(KyTea/Moses)+SentencePiece 8k (shared) 0.1921 0.2086 29.9854 31.2719
(MeCab/Moses)+SentencePiece 8k (shared) 0.1909 0.2049 28.9537 31.4743
(neologd/Moses)+SentencePiece 8k (shared) 0.1938 0.2137 28.8645 31.2985
KyTea/Moses 80k/80k 0.1707 0.2006 23.2161 21.2513
MeCab/Moses 80k/80k 0.1668 0.1892 21.2033 21.2513
neologd/Moses 80k/80k 0.1589 0.1836 18.4768 21.2513
SentencePiece/Moses 8k/80k 0.1727 0.1994 22.9383 21.2513
KyTea/SentencePiece 80k/8k 0.1939 0.2141 23.2161 27.0429
MeCab/SentencePiece 80k/8k 0.1892 0.2077 21.2033 27.0429
neologd/SentencePiece 80k/8k 0.1641 0.1804 18.4768 27.0429
Char 3k (shared) 0.0824 0.0918 33.6963 109.8662

Discussion

  • SentencePiece (Unigram/BPE) outperforms word-based methods (Moses/KyTea/MeCab/neologd) even with a smaller vocabulary (10% of word-based methods).
  • The number of tokens to represent Japanese sentences is almost comparable between SentencePiece (unigram) and KyTea, though the vocabulary of SentencePiece is much smaller. It implies that Sentencepiece can effectively compress the sentences with a smaller vocabulary set.
  • Pretokenization can slightly improve the BLEU scores in English to Japanese. In Japanese to English translation, pretokenization doesn't help to improve BLEU.
  • Neologd shows poor BLEU score. Tokenizing sentences with a large named entity dictionary might not be effective in neural-based text processing.
  • SentencePiece(Unigram) shows slightly better text compression ratio than BPE, but no significant differences in BLEU score.
  • The selection of vocabulary size for SentencePiece is sensitive in English to Japanese. This is probably because the vocabulary size will drastically affect the tokenization results in Japanese which has no explicit spaces between words.

Experiments 2 (subwording with various pre-tokenizations)

Experimental settings

We have evaluated SentencePiece segmentation with the following configurations.

  • Segmentation algorithms:

    • BPE (Byte Pair Encoding) [Sennrich et al.]] (--model_type=bpe)
    • Unigram. Language-model based segmentation. (--model_type=unigram)

  • pretokenization methods:

    • NoPretok: No pretokenization. We train SentencePiece directly from raw sentences (--split_by_whitespace=false).
    • WsPretok: Trains SentencePiece model from the sentences tokenized by whitespaces (--split_by_whitespace=true). When handling CJK, this setting is almost equivalent to NoPretok.
    • MosesPretok: Trains SentencePiece model from sentences tokenized by Moses tokenizer. We used KyTea for Japanese and in-house segmenters for Korean and Chinese respectively.

  • NMT parameters: (Google’s Neural Machine Translation System is applied for all experiments.)

    • 16k shared vocabulary (Shares the same vocabulary for source and target. We train single SentencePiece model by concatenating raw source and target sentences.)
    • Dropout prob: 0.2
    • num nodes: 512
    • num lstms: 8

  • Evaluation metrics:

    • Case-sensitive BLEU on detokenized text with NIST scorer.
    • For CJK, the same word segmenters are applied prior to NIST scorer.
    • No detokenizer is applied for NoPretok and WsPretok, which can directly emit detokenized sentences.
    • Applied Moses detokenizer and in-house rule-based detokenizer (CJK) for MosesPretok.

  • Data sets:

    • KFTT
    • MultiUN (First 5M and next 5k/5k sentences are used for training and development/testing respectively.)
    • WMT16
    • In-house: (Used 5M parallel sentences for training)

NoPretok and WsPretok do not use any language-dependent resources. BPE+MosePretok is almost the same configuration used in [Sennrich et al.] and [Wu et al.].

Results (BLEU scores)

Language Pair BPE(NoPretok) BPE(WsPretok) BPE(MosesPretok) Unigram(NoPretok) Unigram(WsPretok) Unigram(MosesPretok)
KFTT en-ja 0.2796 0.281 0.286 0.2806 0.280 0.2871
KFTT ja-en 0.1943 0.208 0.1967 0.1985 0.2148 0.198
MultiUN ar-en 0.5268 0.5414 0.5381 0.5317 0.5449 0.5401
MultiUN en-ar 0.4039 0.4147 0.4012 0.4084 0.4172 0.3991
MultiUN en-zh 0.4155 0.4186 0.395 0.4214 0.4165 0.399
MultiUN zh-en 0.46 0.4716 0.4806 0.4644 0.4711 0.4759
In house en-ko 0.178 0.1851 0.1893 0.1846 0.1872 0.1890
In house ko-en 0.1786 0.1954 0.1994 0.1845 0.1956 0.2015
WMT16 cs-en 0.1987 0.2252 0.2231 0.2164 0.2228 0.2238
WMT16 de-en 0.3194 0.3348 0.3374 0.3261 0.3375 0.3398
WMT16 en-cs 0.1607 0.1827 0.1812 0.1722 0.1778 0.179
WMT16 en-de 0.2847 0.3029 0.3013 0.2946 0.3000 0.3053
WMT16 en-fi 0.1434 0.1528 0.1499 0.1472 0.1568 0.1517
WMT16 en-ru 0.1884 0.1973 0.1989 0.19 0.1982 0.1903
WMT16 fi-en 0.1775 0.1867 0.1877 0.182 0.1882 0.1865
WMT16 ru-en 0.2042 0.2229 0.2194 0.2087 0.2201 0.2155
  • MosesPretok does not always improve BLEU scores. Comparable accuracy can be obtained without using language-dependent resources in many language pairs.
  • Whitespace pretokenization is a reasonable choice. It does not use language-specific resources.
  • NoPretok shows poor BLEU scores. Unigrams are more robust than BPE when no pretokenizer is applied.