Frances Yung


2019

pdf bib
Acquiring Annotated Data with Cross-lingual Explicitation for Implicit Discourse Relation Classification
Wei Shi | Frances Yung | Vera Demberg
Proceedings of the Workshop on Discourse Relation Parsing and Treebanking 2019

Implicit discourse relation classification is one of the most challenging and important tasks in discourse parsing, due to the lack of connectives as strong linguistic cues. A principle bottleneck to further improvement is the shortage of training data (ca. 18k instances in the Penn Discourse Treebank (PDTB)). Shi et al. (2017) proposed to acquire additional data by exploiting connectives in translation: human translators mark discourse relations which are implicit in the source language explicitly in the translation. Using back-translations of such explicitated connectives improves discourse relation parsing performance. This paper addresses the open question of whether the choice of the translation language matters, and whether multiple translations into different languages can be effectively used to improve the quality of the additional data.

pdf bib
Crowdsourcing Discourse Relation Annotations by a Two-Step Connective Insertion Task
Frances Yung | Vera Demberg | Merel Scholman
Proceedings of the 13th Linguistic Annotation Workshop

The perspective of being able to crowd-source coherence relations bears the promise of acquiring annotations for new texts quickly, which could then increase the size and variety of discourse-annotated corpora. It would also open the avenue to answering new research questions: Collecting annotations from a larger number of individuals per instance would allow to investigate the distribution of inferred relations, and to study individual differences in coherence relation interpretation. However, annotating coherence relations with untrained workers is not trivial. We here propose a novel two-step annotation procedure, which extends an earlier method by Scholman and Demberg (2017a). In our approach, coherence relation labels are inferred from connectives that workers insert into the text. We show that the proposed method leads to replicable coherence annotations, and analyse the agreement between the obtained relation labels and annotations from PDTB and RSTDT on the same texts.

2018

pdf bib
Do Speakers Produce Discourse Connectives Rationally?
Frances Yung | Vera Demberg
Proceedings of the Eight Workshop on Cognitive Aspects of Computational Language Learning and Processing

A number of different discourse connectives can be used to mark the same discourse relation, but it is unclear what factors affect connective choice. One recent account is the Rational Speech Acts theory, which predicts that speakers try to maximize the informativeness of an utterance such that the listener can interpret the intended meaning correctly. Existing prior work uses referential language games to test the rational account of speakers’ production of concrete meanings, such as identification of objects within a picture. Building on the same paradigm, we design a novel Discourse Continuation Game to investigate speakers’ production of abstract discourse relations. Experimental results reveal that speakers significantly prefer a more informative connective, in line with predictions of the RSA model.

2017

pdf bib
Using Explicit Discourse Connectives in Translation for Implicit Discourse Relation Classification
Wei Shi | Frances Yung | Raphael Rubino | Vera Demberg
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Implicit discourse relation recognition is an extremely challenging task due to the lack of indicative connectives. Various neural network architectures have been proposed for this task recently, but most of them suffer from the shortage of labeled data. In this paper, we address this problem by procuring additional training data from parallel corpora: When humans translate a text, they sometimes add connectives (a process known as explicitation). We automatically back-translate it into an English connective and use it to infer a label with high confidence. We show that a training set several times larger than the original training set can be generated this way. With the extra labeled instances, we show that even a simple bidirectional Long Short-Term Memory Network can outperform the current state-of-the-art.

pdf bib
Can Discourse Relations be Identified Incrementally?
Frances Yung | Hiroshi Noji | Yuji Matsumoto
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Humans process language word by word and construct partial linguistic structures on the fly before the end of the sentence is perceived. Inspired by this cognitive ability, incremental algorithms for natural language processing tasks have been proposed and demonstrated promising performance. For discourse relation (DR) parsing, however, it is not yet clear to what extent humans can recognize DRs incrementally, because the latent ‘nodes’ of discourse structure can span clauses and sentences. To answer this question, this work investigates incrementality in discourse processing based on a corpus annotated with DR signals. We find that DRs are dominantly signaled at the boundary between the two constituent discourse units. The findings complement existing psycholinguistic theories on expectation in discourse processing and provide direction for incremental discourse parsing.

2016

pdf bib
Modelling the Usage of Discourse Connectives as Rational Speech Acts
Frances Yung | Kevin Duh | Taku Komura | Yuji Matsumoto
Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning

pdf bib
Modelling the Interpretation of Discourse Connectives by Bayesian Pragmatics
Frances Yung | Kevin Duh | Taku Komura | Yuji Matsumoto
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2015

pdf bib
Crosslingual Annotation and Analysis of Implicit Discourse Connectives for Machine Translation
Frances Yung | Kevin Duh | Yuji Matsumoto
Proceedings of the Second Workshop on Discourse in Machine Translation

pdf bib
Sequential Annotation and Chunking of Chinese Discourse Structure
Frances Yung | Kevin Duh | Yuji Matsumoto
Proceedings of the Eighth SIGHAN Workshop on Chinese Language Processing

pdf bib
EVALution 1.0: an Evolving Semantic Dataset for Training and Evaluation of Distributional Semantic Models
Enrico Santus | Frances Yung | Alessandro Lenci | Chu-Ren Huang
Proceedings of the 4th Workshop on Linked Data in Linguistics: Resources and Applications

2014

pdf bib
Towards a discourse relation-aware approach for Chinese-English machine translation
Frances Yung
Proceedings of the ACL 2014 Student Research Workshop

pdf bib
Analysis and Prediction of Unalignable Words in Parallel Text
Frances Yung | Kevin Duh | Yuji Matsumoto
Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, volume 2: Short Papers

2013

pdf bib
Construction of English MWE Dictionary and its Application to POS Tagging
Yutaro Shigeto | Ai Azuma | Sorami Hisamoto | Shuhei Kondo | Tomoya Kose | Keisuke Sakaguchi | Akifumi Yoshimoto | Frances Yung | Yuji Matsumoto
Proceedings of the 9th Workshop on Multiword Expressions