FewJoint: A Few-shot Learning Benchmark for Joint Language Understanding (2009.08138v3)

Abstract: Few-shot learning (FSL) is one of the key future steps in machine learning and has raised a lot of attention. However, in contrast to the rapid development in other domains, such as Computer Vision, the progress of FSL in Nature Language Processing (NLP) is much slower. One of the key reasons for this is the lacking of public benchmarks. NLP FSL researches always report new results on their own constructed few-shot datasets, which is pretty inefficient in results comparison and thus impedes cumulative progress. In this paper, we present FewJoint, a novel Few-Shot Learning benchmark for NLP. Different from most NLP FSL research that only focus on simple N-classification problems, our benchmark introduces few-shot joint dialogue language understanding, which additionally covers the structure prediction and multi-task reliance problems. This allows our benchmark to reflect the real-word NLP complexity beyond simple N-classification. Our benchmark is used in the few-shot learning contest of SMP2020-ECDT task-1. We also provide a compatible FSL platform to ease experiment set-up.

• The paper introduces a novel benchmark that standardizes few-shot evaluation for joint dialogue language understanding by integrating intent detection and slot tagging tasks.
• The study compares non-fine-tune methods like SepProto and JointProto against a fine-tuned approach (JointProto + Finetune), showing significant improvements in sentence accuracy and slot F1 scores.
• The benchmark leverages a diverse dataset of 6,694 utterances across 59 domains, providing a realistic platform for assessing multi-task learning models in practical NLP applications.

FewJoint: A Few-Shot Learning Benchmark for Joint Language Understanding

The paper "FewJoint: A Few-Shot Learning Benchmark for Joint Language Understanding" presents a significant contribution to the field of NLP by addressing the challenges associated with the development and assessment of Few-Shot Learning (FSL) methods. Few-shot learning, which enables models to learn from a very limited number of examples, has been more rapidly integrated into domains like Computer Vision compared to NLP. This discrepancy is largely attributed to the absence of a standardized benchmark in NLP, one that facilitates a consistent and cumulative evaluation of progress.

FewJoint distinguishes itself as a novel benchmark tailored explicitly for joint dialogic language understanding within the FSL framework. Unlike traditional benchmarks that are often confined to simple N-classification problems, FewJoint encompasses the multifaceted challenges of joint dialogue language understanding. This includes both structure prediction and multi-task learning problems, which are reflective of the complexities encountered in real-world NLP scenarios. Notably, FewJoint is utilized in task-1 of the SMP2020-ECDT contest, further validating its applicability and robustness in practical settings.

To construct this benchmark, the authors gathered 6,694 utterances across 59 different domains from the AIUI platform of iFlytek. This substantial collection stands in contrast to previous works where artificially segmented domains were often employed due to the scarcity of available data. The benchmark allows for the evaluation of few-shot models in scenarios that mimic real-world applications, avoiding the pitfalls associated with data heterogeneity and artificial domain creation.

FewJoint is framed around the specific challenge of task-oriented dialogue language understanding, encapsulating two pivotal sub-tasks: Intent Detection and Slot Tagging. The co-existence of these tasks within a joint framework underlines the multi-task learning aspect of the benchmark, where tasks are not independent but interdependent, as demonstrated in dialogue systems where accurate slot tagging can enhance intent recognition, and vice versa. This multi-task dependency is leveraged by few-shot models to exhibit improved performances, evidenced by the considerable improvement in sentence accuracy and slot F1 scores in baseline evaluations.

The benchmark's evaluation utilizes two key strategies: non-fine-tune based methods, exemplified by SepProto and JointProto; and fine-tune based methods, specifically JointProto + Finetune. The reported results indicate that while JointProto reliably outperforms SepProto, the introduction of fine-tuning with JointProto + Finetune significantly enhances both slot filling and overall sentence accuracy. This highlights the effectiveness of domain-specific tuning in exploiting the limited data typical of few-shot scenarios.

The implications of FewJoint for the NLP community are substantial. By providing a publicly available platform and dataset, FewJoint encourages a standardized evaluation framework for future few-shot learning models. This enables researchers to benchmark their models against a comprehensive dataset that mirrors the complex dynamics of dialogue systems, fostering incremental advancements in the field. Furthermore, FewJoint's approach of integrating multi-task challenges within few-shot learning contexts illustrates a forward-thinking strategy that aligns with the ultimate goal of developing more generalized and robust NLP systems.

In speculating on future developments, FewJoint may inspire further exploration into multi-task and meta-learning strategies, specifically within domains where training data remains scarce. Moreover, as dialogue systems continue to evolve, the benchmark can be expanded or adapted to include emerging complexities such as non-verbal cues and context awareness, ensuring it remains a relevant and cutting-edge tool for NLP researchers.

Overall, FewJoint sets a precedent for future benchmarks in NLP by integrating joint learning tasks and offering a robust platform for the consistent assessment of few-shot learning methods within the complex landscape of natural language understanding.