Multi-facet actions for improved conversational item search refinement

A method for conversational item search using multi-facet actions, the method comprising:

• obtaining a single utterance of a user comprising a conversational search query, the conversational search query comprising a plurality of words identifying an item and a plurality of search refinement terms associated with the item;
• converting the single utterance into a bidirectional sequence of tokens using a natural-language model comprising a set of stacked transformer encoder layers and a conditional random field (CRF) decoder, the bidirectional sequence of tokens representing the plurality of words;
• extracting, from the bidirectional sequence of tokens, a plurality of facet-actions corresponding to the plurality of search refinement terms from the plurality of words, a facet comprising an item attribute, wherein a facet-action comprises a set of filter actions corresponding to the item attribute, each facet-action comprising an entity corresponding to the item attribute, an entity-role identifying a semantic refinement value associated with the entity, and a mapped filter action selected from a set of filter operations including exact, exclude, greater-than, less-than, and range;
• identifying, based on the plurality of facet-actions, a corresponding plurality of multi-facet filters;
• applying the plurality of multi-facet filters to a plurality of items in a catalog, the plurality of multi-facet filters corresponding to the plurality of facet-actions removing items that are irrelevant to the conversational search query;
• identifying a plurality of candidate items from the plurality of items remaining after application of the plurality of multi-facet filters;
• scoring the plurality of candidate items using a set of scoring criteria comprising a semantic-similarity measure between the conversational search query and item-attribute data associated with each candidate item;
• selecting a candidate item from the plurality of candidate items having a highest score;
• and
• generating a search result comprising the selected candidate item, wherein the search result is presented to the user via a user interface.

One or more computer storage devices having computer-executable instructions stored thereon, which, upon execution by a computer, cause the computer to perform operations comprising:

• obtaining a single utterance of a user comprising a conversational search query, the conversational search query comprising a plurality of words identifying an item and a plurality of search refinement terms associated with the item;
• converting the single utterance into a bidirectional sequence of tokens using a natural-language model comprising a set of stacked transformer encoder layers and a conditional random field (CRF) decoder, the bidirectional sequence of tokens representing the plurality of words;
• extracting, from the bidirectional sequence of tokens, a plurality of facet-actions corresponding to the plurality of search refinement terms from the plurality of words, a facet comprising an item attribute, wherein a facet-action comprises a set of filter actions corresponding to the item attribute, each facet-action comprising an entity corresponding to the item attribute, an entity-role identifying a semantic refinement value associated with the entity, and a mapped filter action selected from a set of filter operations including exact, exclude, greater-than, less-than, and range;
• identifying, based on the plurality of facet-actions, a corresponding plurality of multi-facet filters;
• applying the plurality of multi-facet filters to a plurality of items in a catalog, the plurality of multi-facet filters corresponding to the plurality of facet-actions removing items that are irrelevant to the conversational search query;
• identifying a plurality of candidate items from the plurality of items remaining after application of the plurality of multi-facet filters;
• scoring the plurality of candidate items using a set of scoring criteria comprising a semantic-similarity measure between the conversational search query and item-attribute data associated with each candidate item;
• selecting a candidate item from the plurality of candidate items having a highest score;
• and
• generating a search result comprising the selected candidate item, wherein the search result is presented to the user via a user interface.