International Business Machines Corporation

Liat Ein-Dor

Ilya Shnayderman

Noam Slonim

Ranit Aharonov

Artem Spector

Lena Dankin

Michal Shmueli-scheuer

Elron Bandel

A computer-implemented method including: receiving, as input, a dataset comprising training pairs (s, t), wherein each training pair comprises (i) a source sentence s and (ii) a target paraphrase t of the source sentences; at a training stage, training a machine learning model on the dataset, to obtain a trained quality-controlled paraphrase generator model, wherein during the training stage, each of the training pairs is associated with a predicted control vector representing a predicted paraphrase quality of the source sentence in the training pair; and at an inference stage, inferencing the trained quality-controlled paraphrase generator model on an input sentence, wherein the input sentence is associated with an input quality control vector, to obtain an output paraphrase of the input sentence which conforms to the quality control vector.

Quality controlled paraphrase generation

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

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Tel Aviv, Israel

Liat Ein-Dor