Automated generation of improved list-type answers in question answering systems

A system comprising:

• (i) at least one hardware processor;
• and
• (ii) a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by said at least one hardware processor to, automatically:
• receive data comprising:
• a question provided by a user, a list that constitutes a direct answer to the question, and an introductory text to the list,
• use a first machine learning model to classify the introductory text as redundant or nonredundant, based on at least one of the question and the list, wherein the first machine learning model is a language model,
• use a second machine learning model to classify the list as belonging to a certain list type out of multiple list types, based on the list, wherein the second machine learning model is another language model,
• provide to the user:
• (a) the introductory text, only if the introductory text has been classified as nonredundant,
• (b) all or only a subset of items of the list,
• (c) an indication as to a number of non-provided items of the list or a number of all items of the list, if only a subset of the items is being provided in (b), and
• (d) a description of the certain list type;
• and
• train the second machine learning model by:
• defining each of multiple question patterns as corresponding to one of the multiple list types;
• obtaining multiple samples, each comprising a question, an introductory text, and a list;
• for each of the samples:
• matching the respective question with one of the question patterns, and
• weakly labeling the respective list and the respective introductory text as belonging to the list type which corresponds to the matched question pattern;
• and
• adapting a first language model based on the samples and the weak labels of the lists and of the introductory texts to form the second machine learning model.

A computer program product comprising a non-transitory computer-readable storage medium having program code embodied therewith, the program code executable by at least one hardware processor to, automatically:

• receive data comprising:
• a question provided by a user, a list that constitutes a direct answer to the question, and an introductory text to the list;
• use a first machine learning model to classify the introductory text as redundant or nonredundant, based on at least one of the question and the list, wherein the first machine learning model is a language model,
• use a second machine learning model to classify the list as belonging to a certain list type out of multiple list types, based on the list, wherein the second machine learning model is another language model,
• provide to the user:
• (a) the introductory text, only if the introductory text has been classified as nonredundant,
• (b) all or only a subset of items of the list,
• (c) an indication as to a number of non-provided items of the list or a number of all items of the list, if only a subset of the items is being provided in (b), and
• (d) a description of the certain list type;
• and
• train the second machine learning model by:
• defining each of multiple question patterns as corresponding to one of the multiple list types;
• obtaining multiple samples, each comprising a question, an introductory text, and a list;
• for each of the samples:
• matching the respective question with one of the question patterns, and
• weakly labeling the respective list and the respective introductory text as belonging to the list type which corresponds to the matched question pattern;
• and
• adapting a first language model based on the samples and the weak labels of the lists and of the introductory texts to form the second machine learning model.