1,254 research outputs found
A Survey of Paraphrasing and Textual Entailment Methods
Paraphrasing methods recognize, generate, or extract phrases, sentences, or
longer natural language expressions that convey almost the same information.
Textual entailment methods, on the other hand, recognize, generate, or extract
pairs of natural language expressions, such that a human who reads (and trusts)
the first element of a pair would most likely infer that the other element is
also true. Paraphrasing can be seen as bidirectional textual entailment and
methods from the two areas are often similar. Both kinds of methods are useful,
at least in principle, in a wide range of natural language processing
applications, including question answering, summarization, text generation, and
machine translation. We summarize key ideas from the two areas by considering
in turn recognition, generation, and extraction methods, also pointing to
prominent articles and resources.Comment: Technical Report, Natural Language Processing Group, Department of
Informatics, Athens University of Economics and Business, Greece, 201
Question Answering with Subgraph Embeddings
This paper presents a system which learns to answer questions on a broad
range of topics from a knowledge base using few hand-crafted features. Our
model learns low-dimensional embeddings of words and knowledge base
constituents; these representations are used to score natural language
questions against candidate answers. Training our system using pairs of
questions and structured representations of their answers, and pairs of
question paraphrases, yields competitive results on a competitive benchmark of
the literature
On the Evaluation of Semantic Phenomena in Neural Machine Translation Using Natural Language Inference
We propose a process for investigating the extent to which sentence
representations arising from neural machine translation (NMT) systems encode
distinct semantic phenomena. We use these representations as features to train
a natural language inference (NLI) classifier based on datasets recast from
existing semantic annotations. In applying this process to a representative NMT
system, we find its encoder appears most suited to supporting inferences at the
syntax-semantics interface, as compared to anaphora resolution requiring
world-knowledge. We conclude with a discussion on the merits and potential
deficiencies of the existing process, and how it may be improved and extended
as a broader framework for evaluating semantic coverage.Comment: To be presented at NAACL 2018 - 11 page
- …