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Deep LSTM siamese network for text similarity

It is a tensorflow based implementation of deep siamese LSTM network to capture phrase/sentence similarity using character embeddings.

This code provides architecture for learning two kinds of tasks:

  • Phrase similarity using char level embeddings [1] siamese lstm phrase similarity

  • Sentence similarity using char+word level embeddings [2] siamese lstm sentence similarity

For both the tasks mentioned above it uses a multilayer siamese LSTM network and euclidian distance based contrastive loss to learn input pair similairty.

Capabilities

Given adequate training pairs, this model can learn Semantic as well as structural similarity. For eg:

Phrase :

  • International Business Machines = I.B.M
  • Synergy Telecom = SynTel
  • Beam inc = Beam Incorporate
  • Sir J J Smith = Johnson Smith
  • Alex, Julia = J Alex
  • James B. D. Joshi = James Joshi
  • James Beaty, Jr. = Beaty

Sentence :

  • He is smart = He is a wise man.
  • Someone is travelling countryside = He is travelling to a village.
  • She is cooking a dessert = Pudding is being cooked.
  • Microsoft to acquire Linkedin ≠ Linkedin to acquire microsoft

(More examples Ref: semEval dataset)

Categories of pairs, it can learn as similar:

  • Annotations
  • Abbreviations
  • Extra words
  • Similar semantics
  • Typos
  • Compositions
  • Summaries

Training Data

A sample set of learning person name paraphrases have been attached to this repository. To generate full person name disambiguation data follow the steps mentioned at:

https://github.com/dhwajraj/dataset-person-name-disambiguation

Environment

  • numpy 1.11.0
  • tensorflow 1.2.1
  • gensim 1.0.1
  • nltk 3.2.2

How to run

Training

$ python train.py [options/defaults]

options:
  -h, --help            show this help message and exit
  --embedding_dim EMBEDDING_DIM
                        Dimensionality of character embedding (default: 100)
  --dropout_keep_prob DROPOUT_KEEP_PROB
                        Dropout keep probability (default: 0.5)
  --l2_reg_lambda L2_REG_LAMBDA
                        L2 regularizaion lambda (default: 0.0)
  --max_document_words MAX_DOCUMENT_WORDS
                        Max length (left to right max words to consider) in
                        every doc, else pad 0 (default: 100)
  --training_files TRAINING_FILES
                        Comma-separated list of training files (each file is
                        tab separated format) (default: None)
  --hidden_units HIDDEN_UNITS
                        Number of hidden units(default:50)
  --batch_size BATCH_SIZE
                        Batch Size (default: 128)
  --num_epochs NUM_EPOCHS
                        Number of training epochs (default: 200)
  --evaluate_every EVALUATE_EVERY
                        Evaluate model on dev set after this many steps
                        (default: 2000)
  --checkpoint_every CHECKPOINT_EVERY
                        Save model after this many steps (default: 2000)
  --allow_soft_placement [ALLOW_SOFT_PLACEMENT]
                        Allow device soft device placement
  --noallow_soft_placement
  --log_device_placement [LOG_DEVICE_PLACEMENT]
                        Log placement of ops on devices
  --nolog_device_placement

Evaluation

$ python eval.py --model graph#.pb

Performance

  • Training time: (8 core cpu) = 1 complete epoch : 6min 48secs (training requires atleast 30 epochs)
    • Contrastive Loss : 0.0248
  • Evaluation performance : similarity measure for 100,000 pairs (8core cpu) = 1min 40secs
    • Accuracy 91%

Refrerences

  1. Learning Text Similarity with Siamese Recurrent Networks
  2. Siamese Recurrent Architectures for Learning Sentence Similarity