43 |
Does Putting a Linguist in the Loop Improve NLU Data Collection ...
|
|
|
|
BASE
|
|
Show details
|
|
46 |
Say `YES' to Positivity: Detecting Toxic Language in Workplace Communications ...
|
|
|
|
BASE
|
|
Show details
|
|
47 |
Unsupervised Multi-View Post-OCR Error Correction With Language Models ...
|
|
|
|
BASE
|
|
Show details
|
|
48 |
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
|
|
|
|
BASE
|
|
Show details
|
|
49 |
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection ...
|
|
|
|
BASE
|
|
Show details
|
|
50 |
Multi-granularity Textual Adversarial Attack with Behavior Cloning ...
|
|
|
|
BASE
|
|
Show details
|
|
51 |
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning ...
|
|
|
|
BASE
|
|
Show details
|
|
52 |
Towards the Early Detection of Child Predators in Chat Rooms: A BERT-based Approach ...
|
|
|
|
BASE
|
|
Show details
|
|
53 |
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning ...
|
|
|
|
BASE
|
|
Show details
|
|
54 |
WebSRC: A Dataset for Web-Based Structural Reading Comprehension ...
|
|
|
|
BASE
|
|
Show details
|
|
55 |
Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning ...
|
|
|
|
BASE
|
|
Show details
|
|
56 |
Semantic Categorization of Social Knowledge for Commonsense Question Answering ...
|
|
|
|
BASE
|
|
Show details
|
|
57 |
Adversarial Examples for Evaluating Math Word Problem Solvers ...
|
|
|
|
Abstract:
Standard accuracy metrics have shown that Math Word Problem (MWP) solvers have achieved high performance on benchmark datasets. However, the extent to which existing MWP solvers truly understand language and its relation with numbers is still unclear. In this paper, we generate adversarial attacks to evaluate the robustness of state-of-the-art MWP solvers. We propose two methods, Question Reordering and Sentence Paraphrasing to generate adversarial attacks. We conduct experiments across three neural MWP solvers over two benchmark datasets. On average, our attack method is able to reduce the accuracy of MWP solvers by over 40% on these datasets. Our results demonstrate that existing MWP solvers are sensitive to linguistic variations in the problem text. We verify the validity and quality of generated adversarial examples through human evaluation. ...
|
|
URL: https://underline.io/lecture/38384-adversarial-examples-for-evaluating-math-word-problem-solvers https://dx.doi.org/10.48448/xcn3-2415
|
|
BASE
|
|
Hide details
|
|
58 |
Pre-train or Annotate? Domain Adaptation with a Constrained Budget ...
|
|
|
|
BASE
|
|
Show details
|
|
60 |
Learning with Different Amounts of Annotation: From Zero to Many Labels ...
|
|
|
|
BASE
|
|
Show details
|
|
|
|