Reasoning about RoboCup Soccer Narratives

By Hannaneh Hajishirzi, Julia Hockenmaier, Erik T. Mueller, Eyal Amir. 2011

22 citations recorded by the source corpus.

This paper presents an approach for learning to translate simple narratives, i.e., texts (sequences of sentences) describing dynamic systems, into coherent sequences of events without the need for labeled training data. Our approach incorporates domain knowledge in the form of preconditions and effects of events, and we show that it outperforms state-of-the-art supervised learning systems on the task of reconstructing RoboCup soccer games from their commentaries.

Subfields: Artificial Intelligence, Natural Language Processing, Knowledge Graphs & Representation

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. Hannaneh Hajishirzi
2. Ashish Sabharwal
3. Mohit Bansal
4. Luke S. Zettlemoyer
5. Peter Clark
6. Minjoon Seo
7. Percy Liang
8. Ali Farhadi

In Natural Language Processing

1. Hannaneh Hajishirzi
2. Ashish Sabharwal
3. Peter Clark
4. Luke S. Zettlemoyer
5. Mohit Bansal
6. Minjoon Seo
7. Jonathan Berant
8. Ali Farhadi

In Knowledge Graphs & Representation

1. Oren Etzioni
2. Hannaneh Hajishirzi
3. Minjoon Seo
4. Ashish Sabharwal

Citations in the corpus

• Parsing Algebraic Word Problems into Equations (2015, background): “…in the context of a world representation (Branavan et al., 2009; Liang et al., 2009; Chen et al., 2010; Bordes et al., 2010; Feng and Lapata, 2010; Hajishirzi et al., 2011; Matuszek et al., 2012; Hajishirzi et al., 2012; Artzi and Zettlemoyer, 2013; KoncelKedziorski et al., 2014; Yatskar et al.,…”
• Multi-Resolution Language Grounding with Weak Supervision (2014, background): “Early work in understanding soccer commentaries focused on RoboCup soccer (Chen and Mooney, 2008; Chen et al., 2010; Bordes et al., 2010; Hajishirzi et al., 2011) where simple language describes each event, and events are in a one-to-one correspondence with utterances.”
• Solving Geometry Problems: Combining Text and Diagram Interpretation (2015, background): “Our work is part of grounded language acquisition research (Branavan et al., 2012; Vogel and Jurafsky, 2010; Chen et al., 2010; Hajishirzi et al., 2011; Liang et al., 2009; Koncel-Kedziorski et al., 2014; Bordes et al., 2010; Kim and Mooney, 2013; Angeli and Manning, 2014; Hixon et al., 2015;…”