LSDSem 2017: Exploring Data Generation Methods for the Story Cloze Test

By Michael Bugert, Yevgeniy Puzikov, Andrea Rückle, Judith Eckle-Kohler, Teresa Martin, Eugenio Martínez-Cámara, Daniil Sorokin, Maxime Peyrard, Iryna Gurevych. 2017

12 citations recorded by the source corpus.

The Story Cloze test is a recent effort in providing a common test scenario for text understanding systems. As part of the LSDSem 2017 shared task, we present a system based on a deep learning architecture combined with a rich set of manually-crafted linguistic features. The system outperforms all known baselines for the task, suggesting that the chosen approach is promising. We additionally present two methods for generating further training data based on stories from the ROCStories corpus.

Subfields: Artificial Intelligence, General Social Sciences, Natural Language Generation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Rachel Rudinger
2. Benjamin Van Durme
3. Adam Poliak
4. Yejin Choi
5. Aparajita Haldar
6. Yonatan Belinkov
7. Weihao Yu
8. Jiashi Feng

In General Social Sciences

1. Rachel Rudinger
2. Weihao Yu
3. Jiashi Feng
4. Adam Poliak
5. Benjamin Van Durme

Citations in the corpus

• Hypothesis Only Baselines in Natural Language Inference (2018, background): “It has also been shown that features focusing on sentence length, sentiment, and negation are sufficient for achieving high accuracy on this dataset (Schwartz et al., 2017a; Cai et al., 2017; Bugert et al., 2017).”
• Story Ending Selection by Finding Hints From Pairwise Candidate Endings (2019, unknown): “Recently, Story Cloze Test (SCT) [2], [3] was introduced to evaluate the machinery ability of story understanding, story generation, and script learning.”
• ReClor: A Reading Comprehension Dataset Requiring Logical Reasoning (2020, background): “Human-annotated datasets usually contain biases (Schwartz et al., 2017; Cai et al., 2017; Bugert et al., 2017; Poliak et al., 2018; Gururangan et al., 2018; Zellers et al., 2019), which are often exploited by neural network models as shortcut solutions to achieve high testing accuracy.”