14,137 research outputs found
Artificial Error Generation with Machine Translation and Syntactic Patterns.
Shortage of available training data is holding back progress in the area of
automated error detection. This paper investigates two alternative methods for
artificially generating writing errors, in order to create additional
resources. We propose treating error generation as a machine translation task,
where grammatically correct text is translated to contain errors. In addition,
we explore a system for extracting textual patterns from an annotated corpus,
which can then be used to insert errors into grammatically correct sentences.
Our experiments show that the inclusion of artificially generated errors
significantly improves error detection accuracy on both FCE and CoNLL 2014
datasets.Comment: The 12th Workshop on Innovative Use of NLP for Building Educational
Applications (BEA 2017
Wronging a Right: Generating Better Errors to Improve Grammatical Error Detection
Grammatical error correction, like other machine learning tasks, greatly
benefits from large quantities of high quality training data, which is
typically expensive to produce. While writing a program to automatically
generate realistic grammatical errors would be difficult, one could learn the
distribution of naturallyoccurring errors and attempt to introduce them into
other datasets. Initial work on inducing errors in this way using statistical
machine translation has shown promise; we investigate cheaply constructing
synthetic samples, given a small corpus of human-annotated data, using an
off-the-rack attentive sequence-to-sequence model and a straight-forward
post-processing procedure. Our approach yields error-filled artificial data
that helps a vanilla bi-directional LSTM to outperform the previous state of
the art at grammatical error detection, and a previously introduced model to
gain further improvements of over 5% score. When attempting to
determine if a given sentence is synthetic, a human annotator at best achieves
39.39 score, indicating that our model generates mostly human-like
instances.Comment: Accepted as a short paper at EMNLP 201
Domain transfer for deep natural language generation from abstract meaning representations
Stochastic natural language generation systems that are trained from labelled datasets are often domainspecific in their annotation and in their mapping from semantic input representations to lexical-syntactic outputs. As a result, learnt models fail to generalize across domains, heavily restricting their usability beyond single applications. In this article, we focus on the problem of domain adaptation for natural language generation. We show how linguistic knowledge from a source domain, for which labelled data is available, can be adapted to a target domain by reusing training data across domains. As a key to this, we propose to employ abstract meaning representations as a common semantic representation across domains. We model natural language generation as a long short-term memory recurrent neural network encoderdecoder, in which one recurrent neural network learns a latent representation of a semantic input, and a second recurrent neural network learns to decode it to a sequence of words. We show that the learnt representations can be transferred across domains and can be leveraged effectively to improve training on new unseen domains. Experiments in three different domains and with six datasets demonstrate that the lexical-syntactic constructions learnt in one domain can be transferred to new domains and achieve up to 75-100% of the performance of in-domain training. This is based on objective metrics such as BLEU and semantic error rate and a subjective human rating study. Training a policy from prior knowledge from a different domain is consistently better than pure in-domain training by up to 10%
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
Translating Phrases in Neural Machine Translation
Phrases play an important role in natural language understanding and machine
translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is
difficult to integrate them into current neural machine translation (NMT) which
reads and generates sentences word by word. In this work, we propose a method
to translate phrases in NMT by integrating a phrase memory storing target
phrases from a phrase-based statistical machine translation (SMT) system into
the encoder-decoder architecture of NMT. At each decoding step, the phrase
memory is first re-written by the SMT model, which dynamically generates
relevant target phrases with contextual information provided by the NMT model.
Then the proposed model reads the phrase memory to make probability estimations
for all phrases in the phrase memory. If phrase generation is carried on, the
NMT decoder selects an appropriate phrase from the memory to perform phrase
translation and updates its decoding state by consuming the words in the
selected phrase. Otherwise, the NMT decoder generates a word from the
vocabulary as the general NMT decoder does. Experiment results on the Chinese
to English translation show that the proposed model achieves significant
improvements over the baseline on various test sets.Comment: Accepted by EMNLP 201
From Biological to Synthetic Neurorobotics Approaches to Understanding the Structure Essential to Consciousness (Part 3)
This third paper locates the synthetic neurorobotics research reviewed in the second paper in terms of themes introduced in the first paper. It begins with biological non-reductionism as understood by Searle. It emphasizes the role of synthetic neurorobotics studies in accessing the dynamic structure essential to consciousness with a focus on system criticality and self, develops a distinction between simulated and formal consciousness based on this emphasis, reviews Tani and colleagues' work in light of this distinction, and ends by forecasting the increasing importance of synthetic neurorobotics studies for cognitive science and philosophy of mind going forward, finally in regards to most- and myth-consciousness
Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation
Question Generation (QG) is fundamentally a simple syntactic transformation;
however, many aspects of semantics influence what questions are good to form.
We implement this observation by developing Syn-QG, a set of transparent
syntactic rules leveraging universal dependencies, shallow semantic parsing,
lexical resources, and custom rules which transform declarative sentences into
question-answer pairs. We utilize PropBank argument descriptions and VerbNet
state predicates to incorporate shallow semantic content, which helps generate
questions of a descriptive nature and produce inferential and semantically
richer questions than existing systems. In order to improve syntactic fluency
and eliminate grammatically incorrect questions, we employ back-translation
over the output of these syntactic rules. A set of crowd-sourced evaluations
shows that our system can generate a larger number of highly grammatical and
relevant questions than previous QG systems and that back-translation
drastically improves grammaticality at a slight cost of generating irrelevant
questions.Comment: Some of the results in the paper were incorrec
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