International Business Machines Corporation

Matan Vetzler

Orna Raz

Boaz Carmeli

Ateret Anaby-Tavor

Samuel Solomon Ackerman

Ateret Anaby - Tavor

Koren Ran Lazar

Various systems and methods are presented regarding detecting data drift. The data of interest can be batches of utterances received at an interface (e.g., a chatbot). The batches of utterances can be compared with topics present in training data utilized to train a data classifier (e.g., an autoencoder), wherein topics identified in the batches of utterances that are not present in the training data can be considered to be novel topics. The greater the presence of novel topics in a batch of utterances, the greater the divergence of the batch of utterances from the content of the training data. The novel topics can be identified and subsequently applied to the training data such that the data classifier can be re-trained with the novel topics, thereby causing the data classifier to be contemporaneous with the novel topics. In an embodiment, the utterances can be short streams of text, symbols, and suchlike.

Reliable and interpretable drift detection in streams of short texts

A computer-implemented method, system and computer program product for identifying a semantic representation for a set of texts. The set of texts is encoded using a language model to obtain a set of corresponding texts' contextualized embeddings. A centroid of the contextualized embeddings is then calculated. A user-designated number of words from a pre-defined vocabulary of the language model that have their non-contextualized embeddings closest to the centroid are then identified. Furthermore, permutations of the identified words are calculated using an n-gram range. The permutations are then encoded using the language model in a contextualized manner. The encoded permutation from the encoded permutations with the greatest similarity to the centroid is identified. The identified encoded permutation is then assigned as the semantic representation of the set of texts. In this manner, the semantic representation is more effectively identified by expanding the embedding space upon which the semantic representation is chosen.

Identifying a semantic representation for texts by expanding the embedding space

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Givat Shmuel, Israel

Matan Vetzler