This thesis work adopted a model-driven approach to investigate the relationship between different linguistic complexity perspectives for the English language and study how those are learned and encoded by deep learning models at various abstraction levels.
From the theoretical viewpoint of connecting different complexity perspectives using empirical annotations, Chapter 3 analysis highlighted the strong connection between online/offline complexity metrics and length-related linguistic properties of sentences. The relation was further investigated in length-controlled settings, obtaining similar results across online gaze measurements but different for offline perceived complexity annotations. The overall results identify syntagmatic complexity as the primary source of variation in both offline and online complexity perception for readers. However, they also show how the variety in parts and hierarchical structures contributes differently across different complexity perspectives when sentence length is controlled. Another theoretical aspect supported by Chapter 5 experimental results is the role played by cognitive mechanisms other than predictability in shaping human processing patterns on ambiguous constructions like garden-path sentences. In this context, a computational model that accurately predicts the presence or garden-path effects was used as a psycholinguistic subject to provide predictability annotations on standard and atypical constructions. A surprisal-to-reading-times conversion coefficient was then estimated from gaze annotations and surprisal scores on standard constructions. The resulting reading times were used to highlight how the model widely overestimated the magnitude of garden-path effects, following the methodology of Schijndel and Linzen (2020). While results differ significantly from the latter study due to a much larger conversion coefficient, the presence of different accounts for cognitive processing is supported when considering how proportions in predicted magnitudes on different types of constructions do not match the ones reported in recent psycholinguistics literature.
Despite interesting theoretical findings, this work is mostly devoted to interpreting complexity phenomena from a modeling standpoint. Chapter 3 evaluates the encoding of linguistic properties inside neural language models’ representations using probing tasks performed before and after model fine-tuning on complexity-related tasks. Results highlighted the emergence of task-related linguistic properties within the model’s representations after the fine-tuning process, providing evidence for the relation between models’ linguistic skills during training and their performances on morphosyntactically-related tasks. In light of these findings, it can be conjectured that linguistic probes may provide a reasonable estimate of the task-oriented quality of representations for those highly-syntactic tasks. In Chapter 4, the representations learned by neural language models were compared across layers and fine-tuning tasks using representational similarity approaches. The absence of higher similarity scores between complexity-trained models compared to the pre-trained one suggests that training objectives are learned by overfitting annotations and that learned parameters hardly capture information that could be relevant for multiple complexity-related tasks.
Moreover, task framing and the annotation modalities were observed to play a much larger role in defining representational similarity scores rather than the conceptual similarity between tasks. This fact supports the claim that standard optimization procedures used in deep learning are not suitable for this type of concept-driven learning. Finally, Chapter 5 highlighted the inability of standard neural language models in leveraging syntactic cues to improve prediction in the context of garden-path effects. Models fine-tuned on gaze annotations were tested on garden-path test suites to evaluate whether reading time predictions can perform as well as surprisal in identifying garden-path triggers. Results highlight how models heavily overfit gaze annotation and cannot predict the increase in reading times observed in human subjects despite being exposed to the temporary syntactic ambiguity that characterizes garden-path constructions.
Recent trends in transfer learning have profoundly shaped the last few years of research in NLP, leading to astonishing improvements in almost all language-related tasks, including linguistic complexity prediction. Despite all the hype, the fundamental problem behind all computational linguistics research remains: even the most powerful deep learning models do not “understand” language, and their learned representations are “potentially useful, but incomplete, reflections of the actual meaning” they derive from structural training procedures (Bender and Koller 2020). In support of this affirmation, all models leveraged in this study by following closely standard procedures were found lacking in generalization capabilities and hierarchical abstraction, despite their excellent performances on predicting in-domain observations. To conclude with a somewhat cliché affirmation, much work still needs to be done to drive generalizable, hierarchical, and compositional representation learning in language models, enabling proper human-level natural language understanding.
Broader Impact and Ethical Perspectives
The findings described in this thesis work are mostly meta-analytical, and as such, mostly intended to distill theoretical insights and evaluate recent efforts in the natural language processing community. This said, some of the models and procedures described in this work can be clearly beneficial to society. For example, using models trained to predict reading patterns may be used in educational settings to identify difficult passages that can be simplified, improving reading comprehension for students in a fully-personalizable way. This type of technology can also be applied to domain-specific documents such as juridical or medical reports to identify critical areas that can be adapted to improve layman’s understanding. However, it is essential to recognize the potentially malicious usage of such systems. The integration of eye-tracking systems in mobile devices, paired with predictive models presented in this work, could be used to build harmful surveillance systems and advertisement platforms using gaze predictions for extreme behavioral manipulation. Moreover, multiple individuals’ gaze data could be leveraged by autonomous systems to enforce discriminatory practices towards neurodiverse subjects in hardly-detectable ways. In terms of research impact, the experiments presented in this work may provide useful insights into the behavior of neural language models for researchers working in the fields of interpretability in NLP and computational psycholinguistics.
In conclusion, multiple paths to improve and extend the scope of this work were identified during the experimental process, and will be left here as a final note for my future self and for anyone interested in pushing forward research in fields related to this thesis’ topics.
Self-training has recently proven to be very effective for compensating the lack of large labeled datasets in the context of acceptability and complexity prediction (Sarti 2020). In light of these results, it would be interesting to evaluate whether self-training could also improve the performances and generalization of models used for gaze metrics prediction.
Evaluate whether gaze-trained neural language models having undergone a cloze distillation process (Eisape, Zaslavsky, and Levy 2020), combining intuitions from masked language modeling and knowledge distillation (Hinton, Vinyals, and Dean 2015), would produce better results for modeling out-of-distribution garden-path phenomena compared to the somewhat naive approach adopted in this study.
Incorporating gaze metrics prediction in the training objectives of learning models can be interesting to account for human cognitive biases during reading. The crucial aspect is how to get a sufficient amount of annotated data to make this idea scalable for modern language models’ pre-training needs. In this regard, it could be interesting to test the approach by Hollenstein and Zhang (2019) where mean gaze scores are averaged for each type across annotators, effectively providing a way to label input sentences with robust gaze information in an unsupervised manner.
Since eye-tracking metrics are complexity signals with free human supervision, it could be possible to leverage those for simplification and other related tasks in an iterative learning-from-human-feedback paradigm similar to the one described in Stiennon et al. (2020).
It should in principle be possible to use human processing data as a replacement for the self-attention computation. The dot product critically bounds the computational efficiency of attention-based models, and fixed attention has been shown to have a limited negative impact on final results while making inference much faster (Tay et al. 2020). Fixing attention weights using human attention, as measured by eye-tracking metrics, can be an exciting perspective to explore in this context. This idea can be thought of as an application of human attention regularization of LSTM attentional networks for various tasks proposed in Barrett et al. (2018) to Transformers networks.
Would explicitly embedding complexity in the learning process of language models favor hierarchical abstraction? In this perspective, it would be exciting to evaluate whether a model trained on easy-to-hard sentences following language acquisition insights would encode different knowledge in terms of linguistic structures, concept abstraction, and allowances.
Finding better ways to instill useful inductive biases into learning models, especially for syntax-heavy downstream tasks. Concrete examples following this direction may use parsing as a complementary task to keep top-level representations sensible to syntactic changes, as tested in Glavas and Vulic (2020) for natural language understanding, or use hybrid symbolic-neural models to represent syntax as in Zanzotto et al. (2020).
Barrett, Maria, Joachim Bingel, Nora Hollenstein, Marek Rei, and Anders Søgaard. 2018. “Sequence Classification with Human Attention.” In Proceedings of the 22nd Conference on Computational Natural Language Learning, 302–12. Brussels, Belgium: Association for Computational Linguistics. https://doi.org/10.18653/v1/K18-1030.
Bender, Emily M., and Alexander Koller. 2020. “Climbing Towards NLU: On Meaning, Form, and Understanding in the Age of Data.” In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5185–98. Online: Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.463.
Eisape, Tiwalayo, Noga Zaslavsky, and Roger Levy. 2020. “Cloze Distillation Improves Psychometric Predictive Power.” In Proceedings of the 24th Conference on Computational Natural Language Learning, 609–19. Online: Association for Computational Linguistics. https://www.aclweb.org/anthology/2020.conll-1.49.
Glavas, Goran, and Ivan Vulic. 2020. “Is Supervised Syntactic Parsing Beneficial for Language Understanding? An Empirical Investigation.” ArXiv Pre-Print 2008.06788. https://arxiv.org/abs/2008.06788.
Hinton, Geoffrey, Oriol Vinyals, and Jeff Dean. 2015. “Distilling the Knowledge in a Neural Network.” ArXiv Pre-Print 1503.02531. https://arxiv.org/abs/1503.02531.
Hollenstein, Nora, and Ce Zhang. 2019. “Entity Recognition at First Sight: Improving NER with Eye Movement Information.” In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 1–10. Minneapolis, Minnesota: Association for Computational Linguistics. https://doi.org/10.18653/v1/N19-1001.
Sarti, Gabriele. 2020. “UmBERTo-MTSA @ AcCompl-It: Improving Complexity and Acceptability Prediction with Multi-Task Learning on Self-Supervised Annotations.” ArXiv Pre-Print 2011.05197. https://arxiv.org/abs/2011.05197.
Schijndel, Marten van, and Tal Linzen. 2020. “Single-Stage Prediction Models Do Not Explain the Magnitude of Syntactic Disambiguation Difficulty.” PsyArXiv Pre-Print sgbqy. https://psyarxiv.com/sgbqy/.
Stiennon, Nisan, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano. 2020. “Learning to Summarize from Human Feedback.” ArXiv Pre-Print 2009.01325. https://arxiv.org/abs/2009.01325.
Tay, Yi, Dara Bahri, Donald Metzler, D. Juan, Zhe Zhao, and Che Zheng. 2020. “Synthesizer: Rethinking Self-Attention in Transformer Models.” ArXiv Pre-Print 2005.00743. https://arxiv.org/abs/2005.00743.
Zanzotto, Fabio Massimo, Andrea Santilli, Leonardo Ranaldi, Dario Onorati, Pierfrancesco Tommasino, and Francesca Fallucchi. 2020. “KERMIT: Complementing Transformer Architectures with Encoders of Explicit Syntactic Interpretations.” In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (Emnlp), 256–67. Online: Association for Computational Linguistics. https://www.aclweb.org/anthology/2020.emnlp-main.18.