Integrating Semantic Knowledge to Tackle Zero-shot Text Classification

Insufficient or even unavailable training data of emerging classes is a big challenge of many classification tasks, including text classification. Recognising text documents of classes that have never been seen in the learning stage, so-called zero-shot text classification, is therefore difficult and only limited previous works tackled this problem. In this paper, we propose a two-phase framework together with data augmentation and feature augmentation to solve this problem. Four kinds of semantic knowledge (word embeddings, class descriptions, class hierarchy, and a general knowledge graph) are incorporated into the proposed framework to deal with instances of unseen classes effectively. Experimental results show that each and the combination of the two phases achieve the best overall accuracy compared with baselines and recent approaches in classifying real-world texts under the zero-shot scenario.Comment: Accepted NAACL-HLT 201

Semantic Image Synthesis via Adversarial Learning

In this paper, we propose a way of synthesizing realistic images directly with natural language description, which has many useful applications, e.g. intelligent image manipulation. We attempt to accomplish such synthesis: given a source image and a target text description, our model synthesizes images to meet two requirements: 1) being realistic while matching the target text description; 2) maintaining other image features that are irrelevant to the text description. The model should be able to disentangle the semantic information from the two modalities (image and text), and generate new images from the combined semantics. To achieve this, we proposed an end-to-end neural architecture that leverages adversarial learning to automatically learn implicit loss functions, which are optimized to fulfill the aforementioned two requirements. We have evaluated our model by conducting experiments on Caltech-200 bird dataset and Oxford-102 flower dataset, and have demonstrated that our model is capable of synthesizing realistic images that match the given descriptions, while still maintain other features of original images.Comment: Accepted to ICCV 201

The Effect of the China Shock on the 2016 and 2020 US Presidential Elections

Trade liberalization in 2000 opened up the door for increased trade between China and the US, favoring Chinese manufacturers. This period is often referred to as the China shock (Autor, 2013). This paper utilizes data collected from the MIT election lab, FRED, and David Dorn\u27s published data to investigate the effect of the China import shock in the early 2000s on the most recent two US presidential elections. Our analysis, which employs commuting zone-level data, reveals that regions more adversely affected by the China shock were more likely to vote for the Republican Party, while regions that suffered less harm were more likely to vote for the Democratic Party. This research sheds light on the future trade and domestic policies aimed at protecting against economic downturns due to international trade. For instance, policymakers should consider the establishment of assistance and support programs for workers displaced by trade liberalization, such as the Trade Adjustment Assistance (TAA) Program, Unemployment Insurance (UI), and other retraining and compensation policies(Autor et al., 2021). Such policies may influence voters\u27 choices in future elections