Recent developments in active learning algorithms for NLP tasks show promising results in terms of reducing labelling complexity. In this paper we extend this effort to imbalanced datasets; we bridge between the active learning approach of obtaining diverse andinformative examples, and the heuristic of class balancing used in imbalanced datasets. We develop a novel tune-free weighting technique that canbe applied to various existing active learning algorithms, adding a component of class balancing. We compare several active learning algorithms to their modified version on multiple public datasetsand show that when the classes are imbalanced, with manual annotation effort remaining equal the modified version significantly outperforms the original both in terms of the test metric and the number of obtained minority examples. Moreover, when the imbalance is mild or non-existent (classes are completely balanced), our technique does not harm the base algorithms.

When customers search online for a product they are not familiar with, their needs are often expressed through subjective product attributes, such as ”picture quality” for a TV or ”easy to clean” for a sofa. In contrast, the product catalog in online stores includes objective attributes such as ”screen resolution” or ”material”. In this work, we aim to find a link between the objective product catalog and the subjective needs of the customers, to help customers better understand the product space using their own words. We apply correlation-based methods to the store’s product catalog and product reviews in order to find the best potential links between objective and subjective attributes; next, Large Language Models (LLMs) reduce spurious correlations by incorporating common sense and world knowledge (e.g., picture quality is indeed affected by screen resolution, and 8k is the best one). We curate a dataset for this task and show that our combined approach outperforms correlation-only and causation-only approaches.

Pre-trained neural masked language models are often used for predicting a replacement token for a given sequence position, in a cloze-like task. However, this usage is restricted to predicting a single token, from a relatively small pre-trained vocabulary. Recent Sequence2Sequence pre-trained LMs like T5 do allow predicting multi-token completions, but are more expensive to train and run. We show that pre-trained masked language models can be adapted to produce multi-token completions, with only a modest addition to their parameter count. We propose two simple adaptation approaches, trading parameter counts for accuracy. The first method generates multi-token completions from a conditioned RNN. It has a very low parameter count and achieves competitive results. The second method is even simpler: it adds items corresponding to multi-token units to the output prediction matrix. While being higher in parameter count than the RNN method, it also surpasses current state-of-the-art multi-token completion models, including T5-3B, while being significantly more parameter efficient. We demonstrate that our approach is flexible to different vocabularies and domains and can effectively leverage existing pre-trained models available in different domains. Finally, a human evaluation further validates our results and shows that our solution regularly provides valid completions, as well as reasonable correctness for factual-sentence completions.