Personality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait. In this work, we introduce a novel formulation for PIC based on a communication game between a speaker and a listener. The speaker attempts to generate natural language captions while the listener encourages the generated captions to contain discriminative information about the input images and personality traits. In this way, we expect that the generated captions can be improved to naturally represent the images and express the traits. In addition, we propose to adapt the language model GPT2 to perform caption generation for PIC. This enables the speaker and listener to benefit from the language encoding capacity of GPT2. Our experiments show that the proposed model achieves the state-of-the-art performance for PIC.

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 unsupervised setting. 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, this work proposes a Domain-specific Adapter-based Adaptation (DAA) framework to improve the adaptability of BERT-based models for event detection across domains. By explicitly representing data from different domains with separate adapter modules in each layer of BERT, DAA introduces a novel joint representation learning mechanism and a Wasserstein distance-based technique for data selection in adversarial learning to substantially boost the performance on target domains. Extensive experiments and analysis over different datasets (i.e., LitBank, TimeBank, and ACE-05) demonstrate the effectiveness of our approach.

We study the problem of event coreference resolution (ECR) that seeks to group coreferent event mentions into the same clusters. Deep learning methods have recently been applied for this task to deliver state-of-the-art performance. However, existing deep learning models for ECR are limited in that they cannot exploit important interactions between relevant objects for ECR, e.g., context words and entity mentions, to support the encoding of document-level context. In addition, consistency constraints between golden and predicted clusters of event mentions have not been considered to improve representation learning in prior deep learning models for ECR. This work addresses such limitations by introducing a novel deep learning model for ECR. At the core of our model are document structures to explicitly capture relevant objects for ECR. Our document structures introduce diverse knowledge sources (discourse, syntax, semantics) to compute edges/interactions between structure nodes for document-level representation learning. We also present novel regularization techniques based on consistencies of golden and predicted clusters for event mentions in documents. Extensive experiments show that our model achieve state-of-the-art performance on two benchmark datasets.

We study a new problem of cross-lingual transfer learning for event coreference resolution (ECR) where models trained on data from a source language are adapted for evaluations in different target languages. We introduce the first baseline model for this task based on XLM-RoBERTa, a state-of-the-art multilingual pre-trained language model. We also explore language adversarial neural networks (LANN) that present language discriminators to distinguish texts from the source and target languages to improve the language generalization for ECR. In addition, we introduce two novel mechanisms to further enhance the general representation learning of LANN, featuring: (i) multi-view alignment to penalize cross coreference-label alignment of examples in the source and target languages, and (ii) optimal transport to select close examples in the source and target languages to provide better training signals for the language discriminators. Finally, we perform extensive experiments for cross-lingual ECR from English to Spanish and Chinese to demonstrate the effectiveness of the proposed methods.

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