The Natural Language Decathlon: Multitask Learning as Question Answering (1806.08730v1)

Abstract: Deep learning has improved performance on many NLP tasks individually. However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, dataset, and task. We introduce the Natural Language Decathlon (decaNLP), a challenge that spans ten tasks: question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, zero-shot relation extraction, goal-oriented dialogue, semantic parsing, and commonsense pronoun resolution. We cast all tasks as question answering over a context. Furthermore, we present a new Multitask Question Answering Network (MQAN) jointly learns all tasks in decaNLP without any task-specific modules or parameters in the multitask setting. MQAN shows improvements in transfer learning for machine translation and named entity recognition, domain adaptation for sentiment analysis and natural language inference, and zero-shot capabilities for text classification. We demonstrate that the MQAN's multi-pointer-generator decoder is key to this success and performance further improves with an anti-curriculum training strategy. Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting. We also release code for procuring and processing data, training and evaluating models, and reproducing all experiments for decaNLP.

• The paper transforms 10 distinct NLP tasks into a unified question answering framework.
• It introduces MQAN, which uses a multi-pointer-generator decoder and dual coattention for flexible output generation.
• Experimental results highlight state-of-the-art semantic parsing and effective transfer learning across varying NLP benchmarks.

Multitask Learning in NLP: Insights from the Natural Language Decathlon

The paper presented by McCann et al., "The Natural Language Decathlon: Multitask Learning as Question Answering," offers a comprehensive exploration of multitask learning in NLP through the Natural Language Decathlon (decaNLP). This challenge integrates ten distinct tasks into a unified framework, recasting them as instances of question answering. These tasks include question answering, machine translation, summarization, natural language inference, and others. The novelty of this approach lies in its potential to cultivate general NLP models that transcend the limitations of single-task optimization.

Framework and Methodology

decaNLP introduces a radical perspective by realigning diverse tasks as question answering over a context. Each task is transformed into a (question, context, answer) triplet, allowing for a consistent evaluation metric and facilitating multitask learning. The authors introduce the Multitask Question Answering Network (MQAN), which handles the decathlon's tasks without relying on task-specific parameters. The MQAN employs a multi-pointer-generator decoder, enabling the model to selectively draw information from context, questions, or a limited vocabulary, thus accommodating the output requirements of varied tasks.

A particularly innovative aspect of the MQAN is its dual coattention mechanism, which enriches encoding representations by considering interactions between context and question sequences. This approach aligns with recent advances in attention mechanisms, emphasizing the integration of sequence relationships in generating more coherent responses.

Experimental Evaluation

The MQAN, trained on decaNLP's tasks, demonstrates competency across diverse NLP tasks, achieving state-of-the-art results in semantic parsing on WikiSQL and competitive performance in other areas without task-specific adaptations. Notably, the MQAN also exhibits strong transfer learning capabilities, improving translation and named entity recognition results through pretrained weights.

The anti-curriculum learning strategy highlighted in the paper further refines training outcomes. By initially focusing on more complex tasks such as SQuAD before incorporating simpler tasks, MQAN's performance is enhanced, underscoring the importance of task scheduling and complexity in multitask learning environments.

Implications and Future Directions

The implications of the decaNLP benchmark extend beyond immediate performance metrics. By fostering models that handle multiple NLP tasks simultaneously, the research paves the way for more generalized and flexible AI systems capable of adapting to novel tasks with minimal retraining. The capabilities demonstrated by MQAN in zero-shot learning and domain adaptation suggest pathways for models that not only perform diverse tasks proficiently but also adapt to new ones without extensive data or retraining.

Future research directions might explore refining the multitask training paradigms further, investigating the optimal allocation of shared resources, and understanding which sequences of task exposure yield the most robust generalization capabilities. Additionally, integrating larger and more varied datasets could enhance the scope and applicability of such models, contributing to their robustness in even broader linguistic contexts.

Conclusion

The Natural Language Decathlon and the MQAN framework pioneered by McCann et al. signify a progressive step in multitask learning for NLP. By coalescing disparate tasks into a singular question answering framework, the research not only challenges the boundaries of current task-specific models but also establishes a foundation for developing more versatile and adaptive NLP systems. The potential for such frameworks to impact AI development is substantial, encouraging a shift towards models capable of handling a broad spectrum of content without specialization limitations.