Alex Liu

Also published as: Alexander Liu


Cross-Modal Discrete Representation Learning
Alexander Liu | SouYoung Jin | Cheng-I Lai | Andrew Rouditchenko | Aude Oliva | James Glass
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In contrast to recent advances focusing on high-level representation learning across modalities, in this work we present a self-supervised learning framework that is able to learn a representation that captures finer levels of granularity across different modalities such as concepts or events represented by visual objects or spoken words. Our framework relies on a discretized embedding space created via vector quantization that is shared across different modalities. Beyond the shared embedding space, we propose a Cross-Modal Code Matching objective that forces the representations from different views (modalities) to have a similar distribution over the discrete embedding space such that cross-modal objects/actions localization can be performed without direct supervision. We show that the proposed discretized multi-modal fine-grained representation (e.g., pixel/word/frame) can complement high-level summary representations (e.g., video/sentence/waveform) for improved performance on cross-modal retrieval tasks. We also observe that the discretized representation uses individual clusters to represent the same semantic concept across modalities.


Transfer Learning for Entity Recognition of Novel Classes
Juan Diego Rodriguez | Adam Caldwell | Alexander Liu
Proceedings of the 27th International Conference on Computational Linguistics

In this reproduction paper, we replicate and extend several past studies on transfer learning for entity recognition. In particular, we are interested in entity recognition problems where the class labels in the source and target domains are different. Our work is the first direct comparison of these previously published approaches in this problem setting. In addition, we perform experiments on seven new source/target corpus pairs, nearly doubling the total number of corpus pairs that have been studied in all past work combined. Our results empirically demonstrate when each of the published approaches tends to do well. In particular, simpler approaches often work best when there is very little labeled target data, while neural transfer approaches tend to do better when there is more labeled target data.


Detecting Inappropriate Clarification Requests in Spoken Dialogue Systems
Alex Liu | Rose Sloan | Mei-Vern Then | Svetlana Stoyanchev | Julia Hirschberg | Elizabeth Shriberg
Proceedings of the 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)


Modelling Human Clarification Strategies
Svetlana Stoyanchev | Alex Liu | Julia Hirschberg
Proceedings of the SIGDIAL 2013 Conference