Towards Effective Utilization of Pretrained Language Models — Knowledge Distillation from BERT

In the natural language processing (NLP) literature, neural networks are becoming increasingly deeper and more complex. Recent advancements in neural NLP are large pretrained language models (e.g. BERT), which lead to significant performance gains in various downstream tasks. Such models, however, require intensive computational resource to train and are difficult to deploy in practice due to poor inference-time efficiency. In this thesis, we are trying to solve this problem through knowledge distillation (KD), where a large pretrained model serves as teacher and transfers its knowledge to a small student model. We also want to demonstrate the competitiveness of small, shallow neural networks. We propose a simple yet effective approach that transfers the knowledge of a large pretrained network (namely, BERT) to a shallow neural architecture (namely, a bidirectional long short-term memory network). To facilitate this process, we propose heuristic data augmentation methods, so that the teacher model can better express its knowledge on the augmented corpus. Experimental results on various natural language understanding tasks show that our distilled model achieves high performance comparable to the ELMo model (a LSTM based pretrained model) in both single-sentence and sentence-pair tasks, while using roughly 60–100 times fewer parameters and 8–15 times less inference time. Although experiments show that small BiLSTMs are more expressive on natural language tasks than previously thought, we wish to further exploit its capacity through a different KD framework. We propose MKD, a Multi-Task Knowledge Distillation Approach. It distills the student model from different tasks jointly, so that the distilled model learns a more universal language representation by leveraging cross-task data. Furthermore, we evaluate our approach on two different student model architectures, one is bi-attentive LSTM based network, another uses three layer Transformer models. For LSTM based student, our approach keeps the advantage of inference speed while maintaining comparable performance as those specifically designed for Transformer methods. For our Transformer-based student, it does provide a modest gain, and outperforms other KD methods without using external training data

Can Linguistic Knowledge Improve Multimodal Alignment in Vision-Language Pretraining?

The multimedia community has shown a significant interest in perceiving and representing the physical world with multimodal pretrained neural network models, and among them, the visual-language pertaining (VLP) is, currently, the most captivating topic. However, there have been few endeavors dedicated to the exploration of 1) whether essential linguistic knowledge (e.g., semantics and syntax) can be extracted during VLP, and 2) how such linguistic knowledge impact or enhance the multimodal alignment. In response, here we aim to elucidate the impact of comprehensive linguistic knowledge, including semantic expression and syntactic structure, on multimodal alignment. Specifically, we design and release the SNARE, the first large-scale multimodal alignment probing benchmark, to detect the vital linguistic components, e.g., lexical, semantic, and syntax knowledge, containing four tasks: Semantic structure, Negation logic, Attribute ownership, and Relationship composition. Based on our proposed probing benchmarks, our holistic analyses of five advanced VLP models illustrate that the VLP model: i) shows insensitivity towards complex syntax structures and relies on content words for sentence comprehension; ii) demonstrates limited comprehension of combinations between sentences and negations; iii) faces challenges in determining the presence of actions or spatial relationships within visual information and struggles with verifying the correctness of triple combinations. We make our benchmark and code available at \url{https://github.com/WangFei-2019/SNARE/}.Comment: [TL;DR] we design and release the SNARE, the first large-scale multimodal alignment probing benchmark for current vision-language pretrained model

Representation Learning for Texts and Graphs: A Unified Perspective on Efficiency, Multimodality, and Adaptability

[...] This thesis is situated between natural language processing and graph representation learning and investigates selected connections. First, we introduce matrix embeddings as an efficient text representation sensitive to word order. [...] Experiments with ten linguistic probing tasks, 11 supervised, and five unsupervised downstream tasks reveal that vector and matrix embeddings have complementary strengths and that a jointly trained hybrid model outperforms both. Second, a popular pretrained language model, BERT, is distilled into matrix embeddings. [...] The results on the GLUE benchmark show that these models are competitive with other recent contextualized language models while being more efficient in time and space. Third, we compare three model types for text classification: bag-of-words, sequence-, and graph-based models. Experiments on five datasets show that, surprisingly, a wide multilayer perceptron on top of a bag-of-words representation is competitive with recent graph-based approaches, questioning the necessity of graphs synthesized from the text. [...] Fourth, we investigate the connection between text and graph data in document-based recommender systems for citations and subject labels. Experiments on six datasets show that the title as side information improves the performance of autoencoder models. [...] We find that the meaning of item co-occurrence is crucial for the choice of input modalities and an appropriate model. Fifth, we introduce a generic framework for lifelong learning on evolving graphs in which new nodes, edges, and classes appear over time. [...] The results show that by reusing previous parameters in incremental training, it is possible to employ smaller history sizes with only a slight decrease in accuracy compared to training with complete history. Moreover, weighting the binary cross-entropy loss function is crucial to mitigate the problem of class imbalance when detecting newly emerging classes. [...