19,886 research outputs found
A PÄniniÄn Framework for Analyzing Case Marker Errors in English-Urdu Machine Translation
AbstractPanini's KÄraka Theory is solely based on the syntactico-semantic approach to understanding a natural language which takes into consideration the arguments of the verbs. It provides a framework for exhibiting the syntactic relations among constituents in terms of modifier-modified and semantic relations with respect to KÄraka-VibhaktÌȘi (semantic role and postposition).In this paper, it has been argued that PÄniniÄn Dependency Framework can be considered to deal with the MT errors with special reference to case. Firstly, a corpus of approximately 500 English sentences as input have been provided to Google and Bing online MT platforms. Thereafter, all the output sentences in Urdu have been collated in bulk. Thirdly, all the sentences have been evaluated and errors pertaining to case have been categorized based on the Gold Standard. Finally, PÄniniÄn dependency framework has been proposed for addressing the case-related errors for Indian languages
Treebanks gone bad: generating a treebank of ungrammatical English
This paper describes how a treebank of ungrammatical
sentences can be created from a treebank of well-formed sentences. The treebank creation procedure involves the automatic introduction of frequently occurring grammatical errors into the sentences in an existing treebank, and the minimal transformation of the analyses in the treebank so
that they describe the newly created ill-formed sentences.
Such a treebank can be used to test how well a parser is able to ignore grammatical errors in texts (as people can), and can be used to induce a grammar capable of analysing such sentences. This paper also demonstrates the first of these uses
Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus
Many efforts of research are devoted to semantic role labeling (SRL) which is
crucial for natural language understanding. Supervised approaches have achieved
impressing performances when large-scale corpora are available for
resource-rich languages such as English. While for the low-resource languages
with no annotated SRL dataset, it is still challenging to obtain competitive
performances. Cross-lingual SRL is one promising way to address the problem,
which has achieved great advances with the help of model transferring and
annotation projection. In this paper, we propose a novel alternative based on
corpus translation, constructing high-quality training datasets for the target
languages from the source gold-standard SRL annotations. Experimental results
on Universal Proposition Bank show that the translation-based method is highly
effective, and the automatic pseudo datasets can improve the target-language
SRL performances significantly.Comment: Accepted at ACL 202
C-structures and f-structures for the British national corpus
We describe how the British National Corpus (BNC), a one hundred million word balanced corpus of British English, was parsed into Lexical Functional Grammar (LFG) c-structures and f-structures, using a treebank-based
parsing architecture. The parsing architecture uses a state-of-the-art statistical parser and reranker trained on the Penn Treebank to produce context-free phrase structure trees, and an annotation algorithm to automatically annotate
these trees into LFG f-structures. We describe the pre-processing steps which were taken to accommodate the differences between the Penn Treebank and the BNC. Some of the issues encountered in applying the parsing
architecture on such a large scale are discussed. The process of annotating a gold standard set of 1,000 parse trees is described. We present evaluation results obtained by evaluating the c-structures produced by the statistical parser against the c-structure gold standard. We also present the results obtained by evaluating the f-structures produced by the annotation algorithm against an
automatically constructed f-structure gold standard. The c-structures achieve an f-score of 83.7% and the f-structures an f-score of 91.2%
Tuning syntactically enhanced word alignment for statistical machine translation
We introduce a syntactically enhanced word alignment model that is more flexible than state-of-the-art generative word
alignment models and can be tuned according to different end tasks. First of all, this model takes the advantages of
both unsupervised and supervised word alignment approaches by obtaining anchor alignments from unsupervised generative
models and seeding the anchor alignments into a supervised discriminative model. Second, this model offers the flexibility of tuning the alignment according to different
optimisation criteria. Our experiments show that using our word alignment in a Phrase-Based Statistical Machine Translation system yields a 5.38% relative increase
on IWSLT 2007 task in terms of BLEU score
Treebank-based multilingual unification-grammar development
Broad-coverage, deep unification grammar development is time-consuming and costly. This problem can be exacerbated
in multilingual grammar development scenarios. Recently (Cahill et al., 2002) presented a treebank-based methodology
to semi-automatically create broadcoverage, deep, unification grammar resources for English. In this paper we
present a project which adapts this model to a multilingual grammar development scenario to obtain robust, wide-coverage, probabilistic Lexical-Functional Grammars
(LFGs) for English and German via automatic f-structure annotation algorithms based on the Penn-II and TIGER
treebanks. We outline our method used to extract a probabilistic LFG from the TIGER treebank and report on the quality of the f-structures produced. We achieve an f-score of 66.23 on the evaluation of 100 random sentences against a manually constructed gold standard
Content Differences in Syntactic and Semantic Representations
Syntactic analysis plays an important role in semantic parsing, but the
nature of this role remains a topic of ongoing debate. The debate has been
constrained by the scarcity of empirical comparative studies between syntactic
and semantic schemes, which hinders the development of parsing methods informed
by the details of target schemes and constructions. We target this gap, and
take Universal Dependencies (UD) and UCCA as a test case. After abstracting
away from differences of convention or formalism, we find that most content
divergences can be ascribed to: (1) UCCA's distinction between a Scene and a
non-Scene; (2) UCCA's distinction between primary relations, secondary ones and
participants; (3) different treatment of multi-word expressions, and (4)
different treatment of inter-clause linkage. We further discuss the long tail
of cases where the two schemes take markedly different approaches. Finally, we
show that the proposed comparison methodology can be used for fine-grained
evaluation of UCCA parsing, highlighting both challenges and potential sources
for improvement. The substantial differences between the schemes suggest that
semantic parsers are likely to benefit downstream text understanding
applications beyond their syntactic counterparts.Comment: NAACL-HLT 2019 camera read
- âŠ