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Embedding Problems in Neural Language Models

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Recent studies on the geometric properties of contextual embedding space have observed that the distribution of embedding vectors is far from isotropic and occupies a relatively narrow cone space(Mu and Viswanath, 2018; Liu et al., 2019; Zhou et al., 2019; Ethayarajh, 2019;). Gao et al. (2019) named this phenomenon the representation degeneration problem. This degeneration problem results in an increase in the overall cosine similarity between token embeddings, making it difficult for the model to learn semantic relationships between tokens.

Demeter et al. (2020) demonstrated that the norm information of the token embeddings is so dominant that angle information about the feature vector is ignored when calculating the logits in the output layer. Owing to this structural weakness of the embedding space, embeddings with small norms are always assigned with a low probability, which reduces the diversity of the text generated by the model. Anisotropy of the embedding space is a still problem for the pre-trained large language models, and language models with improved isotropic embedding space performs well in downstream tasks(Bis et al. ´ , 2021; Rajaee and Pilehvar, 2021).

Although the problem has been theoretically analyzed in several studies, existing methods are based on the observed phenomena as a result of the problem. To mitigate the phenomena observed from the problem, the post-processing of the embedding vectors(Mu and Viswanath, 2018; Bis et al. ´ , 2021) or regularization terms about the phenomena(Gao et al., 2019; Wang et al., 2019; Wang et al., 2020; Zhang et al., 2020) were introduced. These methods are applied to all token embeddings, so there is the problem of over regularization for the embeddings whose semantic relationship is trained well. Also, methodologies based on the training dynamics of the token embeddings concerning the degeneration problem remain subject to study.

Frequency bias in embedding space is another problem. Ott et al. (2018) conducted a comprehensive study on the under-estimation of rare tokens in neural machine translation. Gong et al. (2018) observed that embeddings in the language model were biased towards frequency and proposed an adversarial training scheme to address this problem.

Reference

  • Jiaqi Mu and Pramod Viswanath. 2018. All-but-thetop: Simple and effective postprocessing for word representations. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 – May 3, 2018, Conference Track Proceedings. OpenReview.net.
  • Tianlin Liu, Lyle Ungar, and João Sedoc. 2019. Unsupervised post-processing of word vectors via conceptor negation. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The ThirtyFirst Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 – February 1, 2019, pages 6778–6785. AAAI Press.
  • Tianyuan Zhou, João Sedoc, and Jordan Rodu. 2019. Getting in shape: Word embedding subspaces. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI 2019, Macao, China, August 10-16, 2019, pages 5478–5484. ijcai.org.
  • Kawin Ethayarajh. 2019. How contextual are contextualized word representations? Comparing the geometry of BERT, ELMo, and GPT-2 embeddings. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 55–65, Hong Kong, China. Association for Computational Linguistics.
  • Jun Gao, Di He, Xu Tan, Tao Qin, Liwei Wang, and TieYan Liu. 2019. Representation degeneration problem in training natural language generation models. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net.
  • David Demeter, Gregory Kimmel, and Doug Downey. 2020. Stolen probability: A structural weakness of neural language models. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2191–2197, Online. Association for Computational Linguistics.
  • Daniel Bis, Maksim Podkorytov, and Xiuwen Liu. 2021. Too much in common: Shifting of embeddings in transformer language models and its implications. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5117–5130, Online. Association for Computational Linguistics.
  • Sara Rajaee and Mohammad Taher Pilehvar. 2021. A cluster-based approach for improving isotropy in contextual embedding space. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 575–584, Online. Association for Computational Linguistics.
  • Dilin Wang, Chengyue Gong, and Qiang Liu. 2019. Improving neural language modeling via adversarial training. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 6555–6565. PMLR.
  • Lingxiao Wang, Jing Huang, Kevin Huang, Ziniu Hu, Guangtao Wang, and Quanquan Gu. 2020. Improving neural language generation with spectrum control.In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net.
  • Zhong Zhang, Chongming Gao, Cong Xu, Rui Miao, Qinli Yang, and Junming Shao. 2020. Revisiting representation degeneration problem in language modeling. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 518–527, Online. Association for Computational Linguistics.
  • Myle Ott, Michael Auli, David Grangier, and Marc’Aurelio Ranzato. 2018. Analyzing uncertainty in neural machine translation. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018, volume 80 of Proceedings of Machine Learning Research, pages 3953–3962. PMLR.
  • ChengYue Gong, Di He, Xu Tan, Tao Qin, Liwei Wang, and Tie-Yan Liu. 2018. FRAGE: frequencyagnostic word representation. In Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada, pages 1341–1352.