esr4 rohit gupta - expert summer school - malaga 2015

S

Use of Paraphrasing to Improve Matching and Retrieval in Translation

Memory Rohit Gupta, University of Wolverhampton

Supervisors: Dr Constantin Orasan, University of Wolverhampton

Prof Josef van Genabith, Saarland University and DFKI Prof Ruslan Mitkov, University of Wolverhampton

Outline

S  Objective

S  Translation Memory S  Incorporating Paraphrasing

S  Human Evaluation

S  Conclusion

Objective

S  Improving matching and retrieval in Translation Memory with the help of advanced language technology. This is achieved by: S  using paraphrases

S  using semantic information

Limitations of current TMs

S  Surface form comparison

S  No or very limited linguistic information


S  Surface form comparison

S  No or very limited linguistic information

S  Paraphrased segments either not retrieved or ranked incorrectly among the retrieved segments


S  Fuzzy scores are really fuzzy S  Input_1: the period laid down in article 4(3)

S  Input_2: the responsible person defined in article 4(3)

S  TM: the duration set forth in article 4(3)

57% fuzzy score as per word-based edit-distance for both input sentences

S

Paraphrasing in TM Matching and Retrieval

Paraphrases

S  PPDB: The paraphrase database (Ganitkevitch et al., 2013)

S  Phrasal and lexical paraphrases

S  L size (2 million)

Concept behind paraphrases

Figure from Ganitkevitch et al., 2013

Trivial Approach

S  Generate additional segments based on paraphrases available

Complexity of Trivial Approach

W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 W14 W15 W16 W17 W18 W19 W20 W21 W22 W23 W24 W25

W1 W2 W3 W4 W5 | W6 W7 W8 W9 W10 |W11 W12 W13 W14 W15 |W16 W17 W18 W19 W20 | W21 W22 W23 W24 W25

5 5 5 5 5

Complexity of Trivial Approach

W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 W14 W15 W16 W17 W18 W19 W20 W21 W22 W23 W24 W25

W1 W2 W3 W4 W5 | W6 W7 W8 W9 W10 |W11 W12 W13 W14 W15 |W16 W17 W18 W19 W20 | W21 W22 W23 W24 W25

5 5 5 5 5

(5+1)^5 -1= 7775 more segments

Our Approach

1.  Dynamic programming and Greedy approximation

2.  Classification of paraphrases

3.  Dealing different paraphrases in different manner

4.  Filtering

Classification of Paraphrases: 4 Types

i.  One word paraphrases

S  “period” => “duration”




ii.  Multiple words but differing in one word

S  “in the period” => “during the period”




ii.  Multiple words but differing in one word

S  “in the period” => “during the period”

iii.  Differing in multiple words but having same number of words

S  “laid down in article” => “set forth in article”


i.  One word paraphrases S  “period” => “duration”

ii.  Multiple words but differing in one word S  “in the period” => “during the period”

iii.  Differing in multiple words but having same number of words S  “laid down in article” => “set forth in article”

iv.  Differing in multiple words with different number of words S  “a reasonable period of time to” => “a reasonable period to”

Example

The period laid down in article 4(3) of decision 468…

Example

The period laid down in article 4(3) of decision 468 …

The period duration time

laid down in

article

4(3) of decision 468 …

Example



laid down referred to provided for

in in by

article article article


Example



laid down referred to provided for

in by

article 2 3


Source length

General Edit-distance Implementation

Insertion cost = Deletion cost = Substitution cost =1

Edit-distance Calculation

0 1 2 3 4 5

TM Input

# the period

laid down in

0 #

1 the

2 period

3 referred

4 to

5 in


0 1 2 3 4 5

TM Input

# the period duration time

laid down in

0 #

1 the

2 period

3 referred

4 to

5 in


0 1 2 3 4 5 31 41 32 42 52

TM Input


laid down in referred to provided for by

0 #

1 the

2 period

3 referred

4 to

5 in


0 1 2 3 4 5 31 41 32 42 52

TM Input



0 # 0

1 the 1

2 period 2

3 referred 3

4 to 4

5 in 5


0 1 2 3 4 5 31 41 32 42 52

TM Input



0 # 0 1

1 the 1 0

2 period 2 1

3 referred 3 2

4 to 4 3

5 in 5 4


0 1 2 3 4 5 31 41 32 42 52

TM Input



0 # 0 1 2

1 the 1 0 1

2 period 2 1 0

3 referred 3 2 1

4 to 4 3 2

5 in 5 4 3


0 1 2 3 4 5 31 41 32 42 52

TM Input



0 # 0 1 2 3

1 the 1 0 1 2

2 period 2 1 0 1

3 referred 3 2 1 1

4 to 4 3 2 2

5 in 5 4 3 3


0 1 2 3 4 5 31 41 32 42 52

TM Input



0 # 0 1 2 3 4 5

1 the 1 0 1 2 3 4

2 period 2 1 0 1 2 3

3 referred 3 2 1 1 2 3

4 to 4 3 2 2 2 3

5 in 5 4 3 3 3 2


0 1 2 3 4 5 31 41 32 42 52

TM Input



0 # 0 1 2 3 4 5 3 4 3 4 5

1 the 1 0 1 2 3 4 2 3 2 3 4

2 period 2 1 0 1 2 3 1 2 1 2 3

3 referred 3 2 1 1 2 3 0 1 1 2 3

4 to 4 3 2 2 2 3 1 0 2 2 3

5 in 5 4 3 3 3 2 2 1 3 3 3


0 1 2 3 4 5 31 41 32 42 52 5

TM Input


laid down in referred to provided for by in

0 # 0 1 2 3 4 5 3 4 3 4 5 5

1 the 1 0 1 2 3 4 2 3 2 3 4 4

2 period 2 1 0 1 2 3 1 2 1 2 3 3

3 referred 3 2 1 1 2 3 0 1 1 2 3 2

4 to 4 3 2 2 2 3 1 0 2 2 3 1

5 in 5 4 3 3 3 2 2 1 3 3 3 0

Computational Complexity

S  Only type (i) and type (ii) paraphrases: S  O(mnlog(p)) , p: paraphrases of types (i) and (ii)

Computational Complexity

S  Only type (i) and type (ii) paraphrases: S  O(mnlog(p)) , p: paraphrases of types (i) and (ii)

S  All paraphrases: S  O(lmn(log(p) + q)) , q: paraphrases of types (iii) and (iv),

l: length of paraphrase

Filtering

1.  Filter out the segments based on length (39%)

Filtering


2.  Filter out the candidates based on baseline edit-distance similarity (39%)

Filtering



3.  Pick the top 100 segments

Filtering



3.  Pick the top 100 segments

4.  Segments within a certain range of similarity with the most similar segment are selected for paraphrasing (35%)

Experiments

S  Corpus Used: S  Europarl V7.0

S  English-German pairs

More results on DGT-TM (English-French) in:

Rohit Gupta and Constantin Orasan 2014. Incorporating Paraphrasing in Translation Memory Matching and Retrieval. In Proceeding of EAMT-2014, Dubrovnik Croatia.

Corpus statistics: Europarl

TM Test

Segments 1,565,194 9,981

Source words 37,824,634 240,916

Target words 36,267,909 230,620

Source average length 24.16 24.13

Target average length 23.17 23.10

Results: Europarl dataset

TH 100 95 90 85 80 75 70

Edit Retrieved 117 127 163 215 257 337 440

+Para Retrieved 16 16 22 33 49 79 102

% Improve 13.68 12.6 13.5 15.35 19.07 23.44 23.18


TH 100 95 90 85 80 75 70

Edit Retrieved 117 127 163 215 257 337 440

+Para Retrieved 16 16 22 33 49 79 102

% Improve 13.68 12.6 13.5 15.35 19.07 23.44 23.18

Rank Change (RC) 9 19 16 25 36 65 97


TH 100 95 90 85 80 75 70

Edit Retrieved 117 127 163 215 257 337 440

+Para Retrieved 16 16 22 33 49 79 102

% Improve 13.68 12.6 13.5 15.35 19.07 23.44 23.18

Rank Change (RC) 9 19 16 25 36 65 97

METEOR-Edit-RC 45.48 46.48 45.59 39.24 37.32 34.02 31.10

METEOR-Para-RC 68.08 67.03 61.09 50.07 44.16 38.35 33.19

BLEU-Edit-RC 31.88 32.37 27.70 21.71 19.32 14.98 12.25

BLEU-Para-RC 52.00 47.92 43.90 31.76 25.24 19.75 15.28


TH 100 [85, 100) [70, 85)

Edit Retrieved 117 127 163

+Para Retrieved 16 30 98

% Improve 13.67 30.61 43.55

Rank Change (RC) 9 14 55

METEOR-Edit-RC 45.48 34.37 25.76

METEOR-Para-RC 68.08 40.00 25.82

BLEU-Edit-RC 31.88 13.18 6.85

BLEU-Para-RC 52.00 17.10 8.37

S

Human Evaluation

Dataset: Human Evaluation

TH 100 [85, 100) [70, 85) Total

Set1 2 6 6 14

Set2 5 4 7 16

Total 7 10 13 30

Evaluations

S  Post-Editing time

S  Keystrokes

S  Subjective Evaluation 2 Options S  A is better

S  B is better

S  Subjective Evaluation 3 Options, Added One more S  Both are equal

Experimental Settings: Post-editing time and

Keystrokes

S  Each file contains segments of both types (ED+PP)

S  Each file is post-edited by 5 translation student

S  German: Native

S  English: C1

Screen: Editing…

Screen: Resting or Start

Results: Keystrokes

532.6 356.2

570.6

468.59

0

200

400

600

800

1000

1200

Edit-Distance Paraphrasing

Num

ber

of K

eyst

roke

s

Set2

Set1

25.23% less keystrokes

Results: Post-Editing Time

520.02 466.44

657.75 603.17

0

200

400

600

800

1000

1200

1400

Edit-Distance Paraphrasing

Pos

t-E

diti

ng T

ime

(Sec

onds

)

Set2

Set1

9.18% time saved

Results: Subjective Evaluation (Two Options, 17 Translators)

66

172 110

162

0 50

100 150 200 250 300 350 400

Edit-Distance is better Paraphrasing is better

Rep

lies

Set2

Set1

Results: Subjective Evaluation (Three Options, Seven Translators)

12 46 40 26

53 33

0

20

40

60

80

100

120

Edit-Distance is better

Paraphrasing is better

Both are equal

Rep

lies

Set2

Set1

H-TER and H-METEOR

Set1 Set2

Edit Distance Paraphrasing Edit Distance Paraphrasing

HMETEOR5 59.82 81.44 69.81 80.60

HTER5 39.72 17.63 27.81 18.71

HMETEOR10 59.82 81.44 69.81 80.61

HTER10 36.93 18.46 27.26 18.40

Segment-wise analysis

S  Statistical significance testing per segment S  Welch-t test (One tailed, p<0.05)



S  Paraphrasing (Keystrokes/Post-Editing Time): S  Twelve segments are significantly better



S  Paraphrasing (Keystrokes/Post-Editing Time): S  Twelve segments are significantly better

S  For ten segments all other evaluations also shows them better



S  Paraphrasing (Keystrokes/Post-Editing Time): S  Twelve segments are significantly better S  For ten segments all other evaluations also shows them better

S  Edit-Distance (Keystrokes/Post-Editing Time): S  Three segments are significantly better S  Not all evaluations shows them better

Conclusion

S  Presented approach to include paraphrasing and machine and retrieval

S  Presented human evaluations

S  In future, we will use deep learning for TM matching and retrieval

Related Publications

S  Rohit Gupta and Constantin Orasan. 2014. Incorporating Paraphrasing in Translation Memory Matching and Retrieval. In Proceeding of EAMT-2014, Dubrovnik Croatia.

S  Rohit Gupta, Constantin Orasan, Marcos Zampieri, Mihaela Vela and Josef van Genabith. 2015. Can Transfer Memories afford not to use paraphrasing? In Proceeding of EAMT-2015, Antalya Turkey.

S  Rohit Gupta, Hanna Bechara, Ismail El Maarouf, and Constantin Orasan. 2014a. UoW: NLP techniques developed at the University of Wolverhampton for Semantic Similarity and Textual Entailment. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval-2014), COLING-2014 Dublin Ireland.

S  Rohit Gupta, Hanna Bechara, and Constantin Orasan. 2014b. Intelligent Translation Memory Matching and Retrieval Metric Exploiting Linguistic Technology. In Proceedings of the thirty sixth Conference on Translating and Computer, London, UK.

References

S  Jane Bradbury and Ismaıl El Maarouf. 2013. An empirical classification of verbs based on Semantic Types: the case of the ’poison’ verbs. In Proceedings of the Joint Symposium on Semantic Processing. Textual Inference and Structures in Corpora, pages 70–74.

S  Juri Ganitkevitch, Van Durme Benjamin, and Chris Callison-Burch. 2013. Ppdb: The paraphrase database. In Proceedings of NAACL-HLT, pages 758–764, Atlanta, Georgia. Association for Computational Linguistics.

S  Marco Marelli, Luisa Bentivogli, Marco Baroni, Raffaella Bernardi, Stefano Menini, and Roberto Zamparelli. 2014a. Semeval-2014 task 1: Evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment. In Proceedings of the 8th International Workshop on Semantic Evaluation (SemEval-2014).

S  Marco Marelli, Stefano Menini, Marco Baroni, Luisa Bentivogli, Raffaella Bernardi, and Roberto Zamparelli. 2014b. A sick cure for the evaluation of compositional distributional semantic models. In Proceedings of LREC 2014.

S  Steinberger, Ralf, Andreas Eisele, Szymon Klocek, Spyridon Pilos, and Patrick Schluter. 2012. DGT- TM: A freely available Translation Memory in 22 languages. LREC, pages 454–459.

Thank you!

esr4 rohit gupta - expert summer school - malaga 2015

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