International Business Machines Corporation

Lena Dankin

Noam Slonim

Ranit Aharonov

Ehud Aharoni

Artem Spector

Ilya Shnayderman

Dan Gutfreund

Ran Levy

Ruty Rinott

Tamar Lavee

Eyal Shnarch

Alon Halfon

Liat Ein-Dor

Ariel Gera

Leshem Choshen

An example system includes a processor to receive a list of sentiment carrying discourse markers. The processor is to select sentences in a text corpus that begin with a discourse marker from the list of sentiment carrying discourse markers followed by a comma. The processor is to remove each discourse marker and comma from a beginning of the selected sentences and labeling each of the sentences with a sentiment associated with to a corresponding removed discourse marker to generate a weakly labeled dataset. The processor is to inter-train a pretrained language model using the generated weakly labeled dataset to generate a sentiment model.

Generating sentiment models using weak labels generated from discourse markers

An example system includes a processor to pre-train a transformer-based language model on a general domain. The processor can inter-train the pre-trained transformer-based language model using partitioning and classification to generate an inter-trained transformer-based pre-trained language model. The processor can then fine-tune the inter-trained transformer-based pre-trained language model on a target task to generate a fine-tuned transformer-based language model.

Inter-training of pre-trained transformer-based language models using partitioning and classification

A method comprising using at least one hardware processor for: receiving a context; identifying evidence with respect to the context in at least one content resource, wherein the identifying comprises: identifying context-free features that generally characterize evidence in the at least one content resource, and identifying context features indicative of the relevance of text segments in the at least one content resource to the context; and outputting a list of said identified evidence.

Context-dependent evidence detection

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Tel Aviv, Israel

Lena Dankin