Most previous studies integrate cognitive language processing signals (e.g., eye-tracking or EEG data) into neural models of natural language processing (NLP) just by directly concatenating word embeddings with cognitive features, ignoring the gap between the two modalities (i.e., textual vs. cognitive) and noise in cognitive features. In this paper, we propose a CogAlign approach to these issues, which learns to align textual neural representations to cognitive features. In CogAlign, we use a shared encoder equipped with a modality discriminator to alternatively encode textual and cognitive inputs to capture their differences and commonalities. Additionally, a text-aware attention mechanism is proposed to detect task-related information and to avoid using noise in cognitive features. Experimental results on three NLP tasks, namely named entity recognition, sentiment analysis and relation extraction, show that CogAlign achieves significant improvements with multiple cognitive features over state-of-the-art models on public datasets. Moreover, our model is able to transfer cognitive information to other datasets that do not have any cognitive processing signals.

中文翻译：

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CogAlign：学习将文本神经表示与认知语言处理信号对齐

大多数先前的研究将认知语言处理信号（例如，眼动追踪或 EEG 数据）整合到自然语言处理 (NLP) 的神经模型中，只是通过直接将词嵌入与认知特征连接起来，而忽略了两种模态（即文本与. 认知）和认知特征中的噪音。在本文中，我们针对这些问题提出了 CogAlign 方法，该方法学习将文本神经表示与认知特征对齐。在 CogAlign 中，我们使用配备有模态鉴别器的共享编码器来交替编码文本和认知输入，以捕捉它们的差异和共性。此外，提出了一种文本感知注意机制来检测与任务相关的信息并避免在认知特征中使用噪声。在三个 NLP 任务上的实验结果，即命名实体识别、情感分析和关系提取，表明 CogAlign 与公共数据集上的最新模型相比，通过多种认知特征实现了显着改进。此外，我们的模型能够将认知信息传输到没有任何认知处理信号的其他数据集。