14 research outputs found
Joint Syntacto-Discourse Parsing and the Syntacto-Discourse Treebank
Discourse parsing has long been treated as a stand-alone problem independent
from constituency or dependency parsing. Most attempts at this problem are
pipelined rather than end-to-end, sophisticated, and not self-contained: they
assume gold-standard text segmentations (Elementary Discourse Units), and use
external parsers for syntactic features. In this paper we propose the first
end-to-end discourse parser that jointly parses in both syntax and discourse
levels, as well as the first syntacto-discourse treebank by integrating the
Penn Treebank with the RST Treebank. Built upon our recent span-based
constituency parser, this joint syntacto-discourse parser requires no
preprocessing whatsoever (such as segmentation or feature extraction), achieves
the state-of-the-art end-to-end discourse parsing accuracy.Comment: Accepted at EMNLP 201
Ranking-Incentivized Quality Preserving Content Modification
The Web is a canonical example of a competitive retrieval setting where many
documents' authors consistently modify their documents to promote them in
rankings. We present an automatic method for quality-preserving modification of
document content -- i.e., maintaining content quality -- so that the document
is ranked higher for a query by a non-disclosed ranking function whose rankings
can be observed. The method replaces a passage in the document with some other
passage. To select the two passages, we use a learning-to-rank approach with a
bi-objective optimization criterion: rank promotion and content-quality
maintenance. We used the approach as a bot in content-based ranking
competitions. Analysis of the competitions demonstrates the merits of our
approach with respect to human content modifications in terms of rank
promotion, content-quality maintenance and relevance.Comment: 10 pages. 8 figures. 3 table
Leveraging Discourse Rewards for Document-Level Neural Machine Translation
Document-level machine translation focuses on the translation of entire
documents from a source to a target language. It is widely regarded as a
challenging task since the translation of the individual sentences in the
document needs to retain aspects of the discourse at document level. However,
document-level translation models are usually not trained to explicitly ensure
discourse quality. Therefore, in this paper we propose a training approach that
explicitly optimizes two established discourse metrics, lexical cohesion (LC)
and coherence (COH), by using a reinforcement learning objective. Experiments
over four different language pairs and three translation domains have shown
that our training approach has been able to achieve more cohesive and coherent
document translations than other competitive approaches, yet without
compromising the faithfulness to the reference translation. In the case of the
Zh-En language pair, our method has achieved an improvement of 2.46 percentage
points (pp) in LC and 1.17 pp in COH over the runner-up, while at the same time
improving 0.63 pp in BLEU score and 0.47 pp in F_BERT.Comment: Accepted at COLING 202
Confusion Modelling - An Estimation by Semantic Embeddings
Approaching the task of coherence assessment of a conversation from its negative perspective ‘confusion’ rather than coherence itself, has been attempted by very few research works. Influencing Embeddings to learn from similarity/dissimilarity measures such as distance, cosine similarity between two utterances will equip them with the semantics to differentiate a coherent and an incoherent conversation through the detection of negative entity, ‘confusion’. This research attempts to measure coherence of conversation between a human and a conversational agent by means of such semantic embeddings trained from scratch by an architecture centralising the learning from the distance between the embeddings. State of the art performance of general BERT’s embeddings and state of the art performance of ConveRT’s conversation specific embeddings in addition to the GLOVE embeddings are also tested upon the laid architecture. Confusion, being a more sensible entity, real human labelling performance is set as the baseline to evaluate the models. The base design resulted in not such a good performance against the human score but the pre-trained embeddings when plugged into the base architecture had performance boosts in a particular order from lowest to highest, through BERT, GLOVE and ConveRT. The intuition and the efficiency of the base conceptual design is proved of its success when the variant having the ConveRT embeddings plugged into the base design, outperformed the original ConveRT’s state of art performance on generating similarity scores. Though a performance comparable to real human performance was not achieved by the models, there witnessed a considerable overlapping between the ConveRT variant and the human scores which is really a great positive inference to be enjoyed as achieving human performance is always the state of art in any research domain. Also, from the results, this research joins the group of works claiming BERT to be unsuitable for conversation specific modelling and embedding works