Question: Can we use LASER for sentence entailment? about laser HOT 5 CLOSED

Could you please specify what you mean with "it doesn't understand sentences with opposite meaning." ?
The system handles very well the XNLI task. This task consists in the classification of two sentence as "related", "neutral" or "opposite"

from laser.

Could you please specify what you mean with "it doesn't understand sentences with opposite meaning." ?

Like "I love Maths instead of English" and "I love English instead of Maths".
I used sentence encoder and tried to calculate the cosine similarity, unfortunately the result was 1.0

from laser.

Something must be wrong in your experiment. It is highly unlikely that the embeddings of two different sentences have a cosine of 1.0
I tried your example and I get a value of about 0.9

from laser.

used #32

MODEL_DIR = f"{LASER}/models"
ENCODER_PATH = f"{MODEL_DIR}/bilstm.93langs.2018-12-26.pt"


encoder = SentenceEncoder(
        ENCODER_PATH,
        max_sentences=None,
        max_tokens=12000,
        sort_kind='mergesort',
        cpu=True)

sentences = [
    'I love Maths instead of English',
    'I love English instead of Maths'
]

embeddings = encoder.encode_sentences(sentences)
print(np.inner(embeddings, embeddings))

from scipy import spatial

dataSetI = embeddings[0]
dataSetII = embeddings[1]
result = 1 - spatial.distance.cosine(dataSetI, dataSetII)
print(result) # result 1.0

from laser.

This is not correct. You need to tokenize and perform BPE before calling encode_sentences().
In particular, without BPE, most of the "full words" are considered as unknown and are mapped to the same token. This explains why the two sentences end up being "identical".

Please see source/embed.py and tasks/embed/README.md

from laser.