I need pointers on the latest research, tools, and techniques for predicting semantic similarity between two phrases.

Problem Statement: Given two propositions A and B with A know to be true, predict if B agrees, contradicts, or is neutral.


A = X is greater than Y | B = Y is greater than X | Result = contradict

A = X is greater than Y | B = Y is less than X | Result = agree

A = X is greater than Y | B = P is less than X | Result = neutral

A = X increases with Y | B = X is directly proportional to Y | Result = agree

A = X is joining Y and Z | B = Y and Z are connected with X | Result = agree

What I have tried

I am looking into Google's Universal Sentence Encoder and alternatives for evaluating semantic similarities with mixed results. Most of the solutions available will give a result of exactly similar for the first example. I am also going through some research papers on grammar-based similarity techniques in natural language.

Any help or direction towards research papers, tools, or libraries is greatly appreciated.

  • $\begingroup$ As an initial thought, it could be helpful if you fed in a model with word dependency relations in the sentence (i.e. is A is the subject or object of the sentence). You can get these relations via dependency parsing (web.stanford.edu/~jurafsky/slp3/15.pdf) $\endgroup$ – shepan6 Sep 14 at 12:55

What you are describing is one of the "standard" NLP problems faced in NLP and it usually referred to as "natural language inference" (NLI), or sometimes also as "textual entailment".

There is plenty of research in this kind of task, and its variants, like cross-lingual NLI (XNLI). I suggest you have a look at nlpprogress (link) or paperswithcode (link) to understand the landscape of benchmark datasets and SoTA models in NLI.

| improve this answer | |
  • $\begingroup$ Thanks for the links. This is exactly what I was looking for. $\endgroup$ – Ashwini Dhekane Sep 14 at 16:14

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