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What you want to look for is called anaphora resolution. You basically keep a record of the past conversation and try and find an antecedent for any occurrences of it, he/she, her/his, etc. You probably want to have a pre-processing step where you substitute the antecedent before passing the input sentence on to the agent.


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Have a look at Named Entity Recognition (NER); these algorithms are mainly concerned with recognising that there is an entity, but often also include normalising the name to a canonical form for information retrieval -- this is what you would need. In a previous job I actually implemented this, using a fuzzy match with variable word order. You would still ...


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Disclaimer: Without the full code, we can only speculate. I encourage you to post the full code on Google Colab or something like this. In the meanwhile, here is my point of view: The Problem Looks like your model has found some "master action" that always leads to zero loss, no matter what the state is. So it's not necessarily bad, it's just ...


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