343 citations · 894 across the 51 of their papers we have counts for
11 papers · 1 filter
Exploring Story Generation with Multi-task Objectives in Variational Autoencoders
Zhuohan Xie, Trevor Cohn, Jey Han Lau
GPT-2 has been frequently adapted in story generation models as it provides powerful generative capability. However, it still fails to generate consistent stories and lacks diversi…
It is Not as Good as You Think! Evaluating Simultaneous Machine Translation on Interpretation Data
Jinming Zhao, Philip Arthur, Gholamreza Haffari +2
Most existing simultaneous machine translation (SiMT) systems are trained and evaluated on offline translation corpora. We argue that SiMT systems should be trained and tested on r…
Unsupervised Cross-Lingual Transfer of Structured Predictors without Source Data
Kemal Kurniawan, Lea Frermann, Philip Schulz +1
Providing technologies to communities or domains where training data is scarce or protected e.g., for privacy reasons, is becoming increasingly important. To that end, we generalis…
Contrastive Learning for Fair Representations
Aili Shen, Xudong Han, Trevor Cohn +2
Trained classification models can unintentionally lead to biased representations and predictions, which can reinforce societal preconceptions and stereotypes. Existing debiasing me…
Fairness-aware Class Imbalanced Learning
Shivashankar Subramanian, Afshin Rahimi, Timothy Baldwin +2
Class imbalance is a common challenge in many NLP tasks, and has clear connections to bias, in that bias in training data often leads to higher accuracy for majority groups at the…
Evaluating Debiasing Techniques for Intersectional Biases
Shivashankar Subramanian, Xudong Han, Timothy Baldwin +2
Bias is pervasive in NLP models, motivating the development of automatic debiasing techniques. Evaluation of NLP debiasing methods has largely been limited to binary attributes in…
Commonsense Knowledge in Word Associations and ConceptNet
Chunhua Liu, Trevor Cohn, Lea Frermann
Humans use countless basic, shared facts about the world to efficiently navigate in their environment. This commonsense knowledge is rarely communicated explicitly, however, unders…
Generating Diverse Descriptions from Semantic Graphs
Jiuzhou Han, Daniel Beck, Trevor Cohn
Text generation from semantic graphs is traditionally performed with deterministic methods, which generate a unique description given an input graph. However, the generation proble…
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation
Jun Wang, Chang Xu, Francisco Guzman +3
Mistranslated numbers have the potential to cause serious effects, such as financial loss or medical misinformation. In this work we develop comprehensive assessments of the robust…
Putting words into the system's mouth: A targeted attack on neural machine translation using monolingual data poisoning
Jun Wang, Chang Xu, Francisco Guzman +4
Neural machine translation systems are known to be vulnerable to adversarial test inputs, however, as we show in this paper, these systems are also vulnerable to training attacks.…
Framing Unpacked: A Semi-Supervised Interpretable Multi-View Model of Media Frames
Shima Khanehzar, Trevor Cohn, Gosia Mikolajczak +2
Understanding how news media frame political issues is important due to its impact on public attitudes, yet hard to automate. Computational approaches have largely focused on class…