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1 Beyond Parallel Corpora Philipp Koehn presented by Huda Khayrallah 1 November 2018

2 1 data and machine learning

3 Supervised and Unsupervised 2 We framed machine translation as a supervised machine learning task training examples with labels here: input sentences with translation structured prediction: output has to be constructed in several steps

4 Supervised and Unsupervised 2 We framed machine translation as a supervised machine learning task training examples with labels here: input sentences with translation structured prediction: output has to be constructed in several steps Unsupervised learning training examples without labels here: just sentences in the input language we will also look at using just sentences output language

5 Supervised and Unsupervised 2 We framed machine translation as a supervised machine learning task training examples with labels here: input sentences with translation structured prediction: output has to be constructed in several steps Unsupervised learning training examples without labels here: just sentences in the input language we will also look at using just sentences output language Semi-supervised learning some labeled training data some unlabeled training data (usually more)

6 Supervised and Unsupervised 2 We framed machine translation as a supervised machine learning task training examples with labels here: input sentences with translation structured prediction: output has to be constructed in several steps Unsupervised learning training examples without labels here: just sentences in the input language we will also look at using just sentences output language Semi-supervised learning some labeled training data some unlabeled training data (usually more) Self-training make predictions on unlabeled training data use predicted labeled as supervised translation data

7 Transfer Learning 3 Learning from data similar to our task

8 Transfer Learning 3 Learning from data similar to our task Other language pairs first, train a model on different language pair then, train on the targeted language pair or: train jointly on both

9 Transfer Learning 3 Learning from data similar to our task Other language pairs first, train a model on different language pair then, train on the targeted language pair or: train jointly on both Multi-Task training train on a related task first e.g., part-of-speeh tagging Share some or all of the components

10 4 using monolingual data

11 Using Monolingual Data 5 Language model trained on large amounts of target language data better fluency of output Key to success of statistical machine translation Neural machine translation integrate neural language model into model create artificial data with backtranslation

12 Adding a Language Model 6 Train a separate language model Add as conditioning context to the decoder

13 Adding a Language Model 6 Train a separate language model Add as conditioning context to the decoder Recall state progression in the decoder decoder state s i embedding of previous output word Ey i 1 input context c i s i = f(s i 1, Ey i 1, c i )

14 Adding a Language Model 6 Train a separate language model Add as conditioning context to the decoder Recall state progression in the decoder decoder state s i embedding of previous output word Ey i 1 input context c i s i = f(s i 1, Ey i 1, c i ) Add hidden state of neural language model s LM i s i = f(s i 1, Ey i 1, c i, s LM i )

15 Adding a Language Model 6 Train a separate language model Add as conditioning context to the decoder Recall state progression in the decoder decoder state s i embedding of previous output word Ey i 1 input context c i s i = f(s i 1, Ey i 1, c i ) Add hidden state of neural language model s LM i Pre-train language model s i = f(s i 1, Ey i 1, c i, s LM i ) Leave its parameters fixed during translation model training

16 Refinements 7 Balance impact of language model vs. translation model

17 Refinements 7 Balance impact of language model vs. translation model Learn a scaling factor (gate) gate LM i = f(s LM i )

18 Refinements 7 Balance impact of language model vs. translation model Learn a scaling factor (gate) gate LM i = f(s LM i ) Use it to scale values of language model state s LM i = gate LM i s LM i

19 Refinements 7 Balance impact of language model vs. translation model Learn a scaling factor (gate) gate LM i = f(s LM i ) Use it to scale values of language model state s LM i = gate LM i s LM i Use this scaled language model state for decoder state s i = f(s i 1, Ey i 1, c i, s LM i )

20 Back Translation 8 Monolingual data is parallel data that misses its other half

21 Back Translation 8 Monolingual data is parallel data that misses its other half Let s synthesize that half reverse system final system

22 Back Translation 9 Steps 1. train a system in reverse language translation 2. use this system to translate translate target side monolingual data synthetic parallel corpus 3. combine generated synthetic parallel data with real parallel data to build the final system Roughly equal amounts of synthetic and real data Useful method of domain adaptation

23 Iterative Back Translation 10 Quality of backtranslation system matters

24 Iterative Back Translation 10 Quality of backtranslation system matters Build a better backtranslation system... with backtranslation

25 Iterative Back Translation 10 Quality of backtranslation system matters Build a better backtranslation system... with backtranslation back system 1 back system 2 final system

26 Iterative Back Translation 11 Example German English Back Final no back-translation *10k iterations (+0.0) *100k iterations (+1.5) convergence (+2.9) re-back-translation (+4.0) * = limited training of back-translation system

27 Round Trip Training 12 We could iterate through steps of train system create synthetic corpus

28 Round Trip Training 12 We could iterate through steps of train system create synthetic corpus Dual learning: train models in both directions together translation models F E and E F take sentence f translate into sentence e translate that back into sentence f training objective: f should match f

29 Round Trip Training 12 We could iterate through steps of train system create synthetic corpus Dual learning: train models in both directions together translation models F E and E F take sentence f translate into sentence e translate that back into sentence f training objective: f should match f Setup could be fooled by just copying (e = f) score e with a language for language E add language model score as cost to training objective

30 Round Trip Training 13 MT F E LM F f MT E F e LM E

31 14 multiple language pairs

32 Multiple Language Pairs 15 There are more than two languages in the world We may want to build systems for many language pairs Typical: train separate models for each Joint training

33 Multiple Input Languages 16 Example German English French English Concatenate training data Joint model benefits from exposure to more English data Shown beneficial in low resource conditions Do input languages have to be related? (maybe not)

34 Multiple Output Languages 17 Example French English French Spanish Concatenate training data Given a French input sentence, how specify output language?

35 Multiple Output Languages 17 Example French English French Spanish Concatenate training data Given a French input sentence, how specify output language? Indicate output language with special tag [ENGLISH] N y a-t-il pas ici deux poids, deux mesures? Is this not a case of double standards? [SPANISH] N y a-t-il pas ici deux poids, deux mesures? No puede verse con toda claridad que estamos utilizando un doble rasero?

36 Zero Shot Translation 18 Example French German German English French English French Spanish MT We want to translate German Spanish English Spanish

37 Zero Shot 19 Train on German English French English French Spanish Specify translation [SPANISH] Messen wir hier nicht mit zweierlei Maß? No puede verse con toda claridad que estamos utilizando un doble rasero?

38 Zero Shot: Hype 20

39 Zero Shot: Reality 21 Bridged: pivot translation Portuguese English Spanish Model 1 and 2: Zero shot training Model 2 + incremental training: use of some training data in language pair

40 Sharing Components 22 So far: generic neural machine translation model Maybe better: separate systems with shared components encoder shared in models with same input language. decoder shared in models with same output language. attention mechanism shared in all models Sharing = same parameters, updates from any language pair training No need to mark output language

41 23 multi-task training

42 Related Tasks 24 Our translation models: generic sequence-to-sequence models Same model used for many other tasks sentiment detection grammar correction semantic inference summarization question answering speech recognition For all these tasks, we need to learn basic properties of language word embeddings contextualize word representations in encoder language model aspects of decoder Why re-invent the wheel each time?

43 Training on Related Tasks 25 Train model on several tasks Maybe shared and task-specific components System learns general facts about language informed by many different tasks useful for many different tasks

44 Pre-Training Word Embeddings 26 Let us keep it simple... Neural machine translation models use word embeddings encoding of input words encoding of output words Word embeddings can be trained on vast amounts of monolingual data pre-train word embeddings and initialize model with them

45 Pre-Training Word Embeddings 26 Let us keep it simple... Neural machine translation models use word embeddings encoding of input words encoding of output words Word embeddings can be trained on vast amounts of monolingual data pre-train word embeddings and initialize model with them Not very successful so far monolingual word embeddings trained on language model objectives for machine translation, different similarity aspects may matter more e.g., teacher and teaching similar in MT, not in LM

46 Pre-Training the Encoder and Decoder 27 Pre-training other components of the translation model Decoder language model, informed by input context pre-train as language model on monolingual data input context vector set to zero

47 Pre-Training the Encoder and Decoder 27 Pre-training other components of the translation model Decoder language model, informed by input context pre-train as language model on monolingual data input context vector set to zero Encoder also structures like a language model (however, not optimized to predict following words) pre-train as language model on monolingual data

48 Multi-Task Training 28 Multiple end-to-end tasks that share common aspects need to encode an input word sequence produce an output word sequence

49 Multi-Task Training 28 Multiple end-to-end tasks that share common aspects need to encode an input word sequence produce an output word sequence May have very different input/output sentiment detection: output is sentiment value part-of-speech tagging: output is tag sequence syntactic parsing: output is recursive parse structure (may be linearized) semantic parsing: output is logical form, database query, or AMR grammar correction: input is error-prone text question answering: needs to be also informed by knowledge base speech recognition: input is sequence of acoustic features

50 Multi-Task Training 28 Multiple end-to-end tasks that share common aspects need to encode an input word sequence produce an output word sequence May have very different input/output sentiment detection: output is sentiment value part-of-speech tagging: output is tag sequence syntactic parsing: output is recursive parse structure (may be linearized) semantic parsing: output is logical form, database query, or AMR grammar correction: input is error-prone text question answering: needs to be also informed by knowledge base speech recognition: input is sequence of acoustic features Input and output in the same language, may be mostly copied grammar correction, automatic post-editing question answering, semantic inference

51 Multi-Task Training 29 Train a single model for all tasks Positive results with joint training of part-of-speech tagging named entity recognition syntactic parsing semantic analysis. Tasks may share just some components

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