alignment demo

demo.py

@@ -0,0 +1,157 @@
+import argparse
+import numpy as np
+import copy
+import torch
+from scipy.spatial.distance import cosine
+from scipy.spatial import KDTree
+
+from allennlp.commands.elmo import ElmoEmbedder
+
+parser = argparse.ArgumentParser(
+    formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+parser.add_argument(
+    '--elmo_weights_path',
+    type=str,
+    default='models/$l_weights.hdf5',
+    help="Path to elmo weights files - use $l as a placeholder for language")
+parser.add_argument(
+    '--elmo_options_path',
+    type=str,
+    default='models/options262.json',
+    help="Path to elmo options file. n_characters in the file should be 262")
+parser.add_argument(
+    '--align_path',
+    type=str,
+    default='models/align/$l_best_mapping.pth',
+    help="Path to elmo options file. n_characters in the file should be 262")
+parser.add_argument(
+    '-l1',
+    '--language1',
+    type=str,
+    default='en',
+    help="language of sentence 1")
+parser.add_argument(
+    '-s1',
+    '--sent1',
+    type=str,
+    default=
+    'A house cat is valued by humans for companionship and for its ability to hunt rodents.',
+    help="sentence in language 1")
+parser.add_argument(
+    '-w1',
+    '--word1',
+    type=str,
+    default='cat',
+    help=
+    "Examined word from the sentence of language 1 (first occurrence will be used)"
+)
+parser.add_argument(
+    '-l2',
+    '--language2',
+    type=str,
+    default='es',
+    help="language of sentence 2")
+parser.add_argument(
+    '-s2',
+    '--sent2',
+    type=str,
+    default=
+    'el gato doméstico está incluido en la lista 100 de las especies exóticas invasoras más dañinas del mundo.',
+    help="sentence in language 2")
+parser.add_argument(
+    '-w2',
+    '--word2',
+    type=str,
+    default='gato',
+    help=
+    "Examined word from the sentence of language 2 (first occurrence will be used)"
+)
+parser.add_argument(
+    '--layer', type=int, default=1, help="Layer of Elmo to compute for")
+parser.add_argument(
+    '-c', '--cuda_device', type=int, default=-1, help="Cuda device")
+args = parser.parse_args()
+
+
+def parse_config(args):
+    '''
+    replace $l with args.lang
+    print args
+    '''
+
+    new_args = copy.deepcopy(args)
+    for k in vars(args):
+        val = getattr(args, k)
+        if type(val) is str and "$l" in val:
+            new_val = val.replace("$l", args.language1)
+            new_k = "{}_{}".format(k, "l1")
+            setattr(new_args, new_k, new_val)
+
+            new_val = val.replace("$l", args.language2)
+            new_k = "{}_{}".format(k, "l2")
+            setattr(new_args, new_k, new_val)
+
+    print('-' * 30)
+    for k in vars(new_args):
+        print("{}: {}".format(k, getattr(new_args, k)))
+    print('-' * 30)
+
+    return new_args
+
+
+def get_sent_embeds(sent, elmo_options_file, elmo_weights_file, layer,
+                    cuda_device):
+    '''
+    Get the embeddings of the sentence words.
+    sent - list of tokens
+    elmo_options_file - json for model. n_characters should be 262
+    elmo_weights_file - saved model
+    layer - what layer of ELMo to output
+    cuda_device - cuda device
+
+    returns a numpy array with the embeddings per token for the selected layer
+    '''
+    elmo = ElmoEmbedder(elmo_options_file, elmo_weights_file, cuda_device)
+    s_embeds = elmo.embed_sentence(sent)
+    layer_embeds = s_embeds[layer,:,:]
+    return layer_embeds
+
+def analyze_sents(embeds_l1, embeds_l2, sent1, sent2, w1_ind, w2_ind, k=5):
+    kdt = KDTree(embeds_l1)
+    emb2 = embeds_l2[w2_ind]
+    top_k_inds = kdt.query(emb2, k)[1]
+    top_k_words = [sent1[i] for i in top_k_inds]
+    print('Nearest {} neighbors for {} in "{}":\n{}'.format(k, sent2[w2_ind], ' '.join(sent1), ' ,'.join(top_k_words)))
+
+    emb1 = embeds_l1[w1_ind]
+    dist = cosine(emb1, emb2)
+    print("Cosine distance between {} and {}: {}".format(sent1[w1_ind], sent2[w2_ind],dist))
+
+if __name__ == '__main__':
+    args = parse_config(args)
+
+    # Language 1
+    sent1_tokens = args.sent1.strip().split()
+    w1_ind = sent1_tokens.index(args.word1)
+    s1_embeds = get_sent_embeds(sent1_tokens, args.elmo_options_path,
+                                args.elmo_weights_path_l1, args.layer,
+                                args.cuda_device)
+
+    align1 = torch.load(args.align_path_l1)
+    s1_embeds_aligned = np.matmul(s1_embeds, align1.transpose())
+
+    # Language 2
+    sent2_tokens = args.sent2.strip().split()
+    w2_ind = sent2_tokens.index(args.word2)
+    s2_embeds = get_sent_embeds(sent2_tokens, args.elmo_options_path,
+                                args.elmo_weights_path_l2, args.layer,
+                                args.cuda_device)
+
+    align2 = torch.load(args.align_path_l2)
+    s2_embeds_aligned = np.matmul(s2_embeds, align2.transpose())
+
+    print("--- Before alignment:")
+    analyze_sents(s1_embeds, s2_embeds, sent1_tokens, sent2_tokens, w1_ind, w2_ind)
+
+    print("\n--- After alignment:")
+    analyze_sents(s1_embeds_aligned, s2_embeds_aligned, sent1_tokens, sent2_tokens, w1_ind, w2_ind)

get_alignments.sh

@@ -0,0 +1,10 @@
+mkdir models
+mkdir models/align
+cd models/align
+wget https://www.dropbox.com/s/nufj4pxxgv5838r/en_best_mapping.pth
+wget https://www.dropbox.com/s/6kqot8ssy66d5u0/es_best_mapping.pth
+wget https://www.dropbox.com/s/0zdlanjhajlgflm/fr_best_mapping.pth
+wget https://www.dropbox.com/s/gg985snnhajhm5i/it_best_mapping.pth
+wget https://www.dropbox.com/s/skdfz6zfud24iup/pt_best_mapping.pth
+wget https://www.dropbox.com/s/o7v64hciyifvs8k/sv_best_mapping.pth
+wget https://www.dropbox.com/s/u9cg19o81lpm0h0/de_best_mapping.pth