How Transferable are Neural Networks in NLP Applications? (1603.06111v2)

Abstract: Transfer learning is aimed to make use of valuable knowledge in a source domain to help model performance in a target domain. It is particularly important to neural networks, which are very likely to be overfitting. In some fields like image processing, many studies have shown the effectiveness of neural network-based transfer learning. For neural NLP, however, existing studies have only casually applied transfer learning, and conclusions are inconsistent. In this paper, we conduct systematic case studies and provide an illuminating picture on the transferability of neural networks in NLP.

• The paper demonstrates that transfer learning in NLP is most effective when source and target tasks share semantic similarity.
• It compares INIT and MULT methods using CNNs and LSTMs across six datasets, revealing that layer-specific transfers vary in effectiveness.
• The results indicate that optimal learning rates and transfer timing play a crucial role in balancing model performance and preserving pretrained knowledge.

An Analysis of Transferability of Neural Networks in NLP Applications

The paper "How Transferable are Neural Networks in NLP Applications?" presents an in-depth investigation of transfer learning within the field of neural network-based NLP. The research addresses an essential aspect of contemporary machine learning, where transfer learning involves leveraging knowledge from a source domain to enhance model performance in a target domain. While this paradigm has shown substantial efficacy in image processing, its success and understanding in NLP have been uneven and insufficiently explored.

Core Contributions and Methodologies

The research distinguishes two primary scenarios of transfer: (1) tasks with semantic similarity or equivalence but different datasets and (2) tasks that are semantically distinct but share similar neural topologies, thus permitting parameter transfer. Additionally, two transfer methods are rigorously examined: parameter initialization (INIT) and multi-task learning (MULT), with a combination of both also explored.

• INIT involves pretraining on a source task and subsequently fine-tuning or fixing the model for a target task. This method focuses on effective initialization using pretrained parameters from related tasks.
• MULT, contrastingly, simultaneously trains both source and target tasks by optimizing a shared cost function, thus embedding a multi-task learning framework.

The paper utilizes convolutional neural networks (CNNs) and long short-term memory (LSTM) networks across six datasets, chosen to represent various sentence classification tasks, such as sentiment analysis, question classification, natural language inference, and paraphrase detection.

Key Findings and Observations

1. Semantic Similarity as a Determinant: The transferability of neural networks in NLP predominantly hinges on the semantic relatedness of the source and target tasks. This differs notably from image processing domains, where neural features transfer more readily across tasks. In semantically similar tasks, significant improvements were observed, affirming the viability of transfer learning. However, in semantically divergent tasks, improvements were negligible or absent, highlighting the challenges in transferring across unrelated semantic spaces.
2. Layer-Wise Transferability: The paper identifies that while output layers are dataset-specific and minimally transferable, word embeddings are potentially transferable, particularly in semantically diverse tasks. Embeddings capture foundational linguistic features that can apply across varied tasks, albeit with varying degrees of success in higher network layers.
3. Comparison of INIT and MULT: INIT and MULT proved generally comparable in enhancing task performance, with no consistent superior performance from either method across all tasks. The combination of MULT+INIT did not yield further performance gains, suggesting that while both have strengths, they do not necessarily complement each other synergistically in the tested configurations.
4. Learning Rate and Timing in Transfer: It was observed that larger learning rates could expedite training post-transfer without damaging transferred knowledge, contradicting concerns that such rates might detrimentally alter pretrained parameters. Additionally, the optimal timing for parameter transfer does not universally align with peak performance on source tasks, especially for semantically different task pairs.

Implications and Prospective Directions

This paper offers critical insights into the nuanced transferability mechanics within NLP, suggesting that semantic alignment between tasks is a pivotal factor in successful knowledge transfer. Furthermore, it establishes a groundwork that challenges the assumption of one-size-fits-all in transfer learning approaches like INIT and MULT, prompting a tailored strategy based on task semantics and dataset characteristics.

Future research may focus on refining multi-task learning strategies, exploring adaptive methods that consider semantic alignment dynamically, and expanding the model architectures and tasks to test the generalizability of these findings. Investigating transfer learning in more linguistically diverse settings and incorporating domain-specific adaptations could further enhance the efficacy and applicability of neural transfer learning in NLP.