Name
Abstract | ||
|---|---|---|
Textual Inference is a research trend in Natural Language Processing (NLP) that has recently received a lot of attention by the scientific community. Textual Entailment (TE) is a specific task in Textual Inference that aims at determining whether a hypothesis is entailed by a text. This paper employs the Child-Sum Tree-LSTM for solving the challenging problem of textual entailment. Our approach is simple and able to generalize well without excessive parameter optimization. Evaluation done on SNLI, SICK and other TE datasets shows the competitiveness of our approach. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-67468-1_2 | BNAIC 2016: ARTIFICIAL INTELLIGENCE |
Keywords | DocType | Volume |
Child-Sum Tree LSTM,Information retrieval,Textual entailment | Conference | 765 |
ISSN | Citations | PageRank |
1865-0929 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kolawole John Adebayo | 1 | 0 | 1.01 |
| Luigi Di Caro | 2 | 195 | 35.21 |
| Livio Robaldo | 3 | 269 | 33.46 |
| Guido Boella | 4 | 1867 | 162.59 |