What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation

Abstract

Heavily pre-trained transformer models such as BERT have recently shown to be remarkably powerful at language modelling by achieving impressive results on numerous downstream tasks. It has also been shown that they are able to implicitly store factual knowledge in their parameters after pre-training. Understanding what the pre-training procedure of LMs actually learns is a crucial step for using and improving them for Conversational Recommender Systems (CRS). We first study how much off-the-shelf pre-trained BERT "knows" about recommendation items such as books, movies and music. In order to analyze the knowledge stored in BERT's parameters, we use different probes that require different types of knowledge to solve, namely content-based and collaborative-based. Content-based knowledge is knowledge that requires the model to match the titles of items with their content information, such as textual descriptions and genres. In contrast, collaborative-based knowledge requires the model to match items with similar ones, according to community interactions such as ratings. We resort to BERT's Masked Language Modelling head to probe its knowledge about the genre of items, with cloze style prompts. In addition, we employ BERT's Next Sentence Prediction head and representations' similarity to compare relevant and non-relevant search and recommendation query-document inputs to explore whether BERT can, without any fine-tuning, rank relevant items first. Finally, we study how BERT performs in a conversational recommendation downstream task. Overall, our analyses and experiments show that: (i) BERT has knowledge stored in its parameters about the content of books, movies and music; (ii) it has more content-based knowledge than collaborative-based knowledge; and (iii) fails on conversational recommendation when faced with adversarial data.

Accepted for publication at RecSys'20

References in corpus (6)

• Overcoming catastrophic forgetting in neural networks
• Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
• End-to-End Open-Domain Question Answering with BERTserini
• Response Ranking with Deep Matching Networks and External Knowledge in Information-seeking Conversation Systems
• Learning Latent Vector Spaces for Product Search
• Diagnosing BERT with Retrieval Heuristics

Cited by in corpus (5)

• Uncovering ChatGPT's Capabilities in Recommender Systems
• Large Language Models as Zero-Shot Conversational Recommenders
• Towards Retrieval-based Conversational Recommendation
• Alleviating the Long-Tail Problem in Conversational Recommender Systems
• ABXI: Invariant Interest Adaptation for Task-Guided Cross-Domain Sequential Recommendation