Distributionally Robust Fair Principal Components via Geodesic Descents
Introduction Machine learning models are ubiquitous in our daily lives and supporting the decision-m(...)
22/12/2021 Natural Language Processing
Nghia Ngo Trung (Research Resident)
Event detection (ED) is an important component in the overall event extraction pipeline, which plays a crucial role in any natural language understanding system. Tackling ED problem involves both locating the event triggers and categorizing them into specific event types, therefore can be a quite challenging task due to the intricate dependency among triggers, events, and contexts in linguistic data. The complication is further amplified by domain shift problem when texts are collected from multiple different domains. In this paper, our work explores the general problem of domain adaptation for ED where data comes from two different source and target domains. In particular, we focus on the unsupervised learning setting that requires no annotation for target data, and the model has to learn to make use of both labeled source and unlabeled target samples to improve its performance on target domain.
Due to the multi-dimensional variation of textual data, detection of event triggers from new domains can become a lot more challenging. This prompts a need to research on domain adaptation methods for event detection task, especially for the most practical setting of supervised learning. Recently, large transformer-based language models, e.g. BERT, have become essential to achieve top performance for event detection. However, their unwieldy nature also prevents effective adaptation across domains. To this end, we propose a Domain-specific Adapter-based Adaptation (DAA) framework to improve the adaptability of BERT-based models for event detection across domains.
The overall architecture of our framework is depicted in figure 1. First, adapter-based fine-tuning (Houlsby et al., 2019) is leveraged to efficiently create domain-specific representations without having to duplicate the whole model. These representations are then trained to extract domain-invariant features through a modified domain-adversarial neural network (DANN) (Ganin et al., 2015). Finally, a data selection mechanism is employed based on the Wasserstein distance (Shen et al., 2018) between samples of source and target domain to improve the robustness of the framework.
The whole learning process is alternated between divergence steps and representation steps. The former is repeated k times to accurately approximate the divergences between domains, whereas the latter uses the previously computed distance to update parameters of adapters and other downstream tasks.
The Automatic Content Extraction 2005 (ACE-05) dataset is a densely annotated corpus collected from 6 different domains: Newswire (nw) – 20%, Broadcast news (bn) – 20%, Broadcast conversation (bc) – 15%, Weblog (wl) – 15%, Usenet Newsgroups (un) – 15%, Conversational Telephone Speech (cts) – 15%. Events of the dataset are categorized into 33 types.
We combine samples from two closely related domains, nw and bn, to create a sizable labeled training source dataset. Then, each of the other domains is considered the target domain of a single adaptation setting. Performance of each trained model is measured in precision (P), recall (R), and F1 scores.
Table 1 showcases the results of our event detection experiment. The main conclusions from the table include:
We present a novel framework for ED in the unsupervised domain adaptation setting that effectively leverages the generalization capability of large pre-trained language models through a shared-private adapter-based architecture. A layer-wise domain-adversarial training process combined with Wasserstein-based data selection addresses the discrepancy between domains and produces domain-invariant representations. The proposed model achieves state-of-the-art results on several adaptation settings across multiple domains.
Nghia Ngo Trung (Research Resident)