Daniel Beck

Also published as: Daniel Emilio Beck


2021

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On the (In)Effectiveness of Images for Text Classification
Chunpeng Ma | Aili Shen | Hiyori Yoshikawa | Tomoya Iwakura | Daniel Beck | Timothy Baldwin
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Images are core components of multi-modal learning in natural language processing (NLP), and results have varied substantially as to whether images improve NLP tasks or not. One confounding effect has been that previous NLP research has generally focused on sophisticated tasks (in varying settings), generally applied to English only. We focus on text classification, in the context of assigning named entity classes to a given Wikipedia page, where images generally complement the text and the Wikipedia page can be in one of a number of different languages. Our experiments across a range of languages show that images complement NLP models (including BERT) trained without external pre-training, but when combined with BERT models pre-trained on large-scale external data, images contribute nothing.

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Generating Diverse Descriptions from Semantic Graphs
Jiuzhou Han | Daniel Beck | Trevor Cohn
Proceedings of the 14th International Conference on Natural Language Generation

Text generation from semantic graphs is traditionally performed with deterministic methods, which generate a unique description given an input graph. However, the generation problem admits a range of acceptable textual outputs, exhibiting lexical, syntactic and semantic variation. To address this disconnect, we present two main contributions. First, we propose a stochastic graph-to-text model, incorporating a latent variable in an encoder-decoder model, and its use in an ensemble. Second, to assess the diversity of the generated sentences, we propose a new automatic evaluation metric which jointly evaluates output diversity and quality in a multi-reference setting. We evaluate the models on WebNLG datasets in English and Russian, and show an ensemble of stochastic models produces diverse sets of generated sentences while, retaining similar quality to state-of-the-art models.

2020

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Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts
Lucia Specia | Daniel Beck
Proceedings of the 28th International Conference on Computational Linguistics: Tutorial Abstracts

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Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association
Maria Kim | Daniel Beck | Meladel Mistica
Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association

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Information Extraction from Legal Documents: A Study in the Context of Common Law Court Judgements
Meladel Mistica | Geordie Z. Zhang | Hui Chia | Kabir Manandhar Shrestha | Rohit Kumar Gupta | Saket Khandelwal | Jeannie Paterson | Timothy Baldwin | Daniel Beck
Proceedings of the The 18th Annual Workshop of the Australasian Language Technology Association

‘Common Law’ judicial systems follow the doctrine of precedent, which means the legal principles articulated in court judgements are binding in subsequent cases in lower courts. For this reason, lawyers must search prior judgements for the legal principles that are relevant to their case. The difficulty for those within the legal profession is that the information that they are looking for may be contained within a few paragraphs or sentences, but those few paragraphs may be buried within a hundred-page document. In this study, we create a schema based on the relevant information that legal professionals seek within judgements and perform text classification based on it, with the aim of not only assisting lawyers in researching cases, but eventually enabling large-scale analysis of legal judgements to find trends in court outcomes over time.

2019

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Neural Speech Translation using Lattice Transformations and Graph Networks
Daniel Beck | Trevor Cohn | Gholamreza Haffari
Proceedings of the Thirteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-13)

Speech translation systems usually follow a pipeline approach, using word lattices as an intermediate representation. However, previous work assume access to the original transcriptions used to train the ASR system, which can limit applicability in real scenarios. In this work we propose an approach for speech translation through lattice transformations and neural models based on graph networks. Experimental results show that our approach reaches competitive performance without relying on transcriptions, while also being orders of magnitude faster than previous work.

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Modelling Uncertainty in Collaborative Document Quality Assessment
Aili Shen | Daniel Beck | Bahar Salehi | Jianzhong Qi | Timothy Baldwin
Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)

In the context of document quality assessment, previous work has mainly focused on predicting the quality of a document relative to a putative gold standard, without paying attention to the subjectivity of this task. To imitate people’s disagreement over inherently subjective tasks such as rating the quality of a Wikipedia article, a document quality assessment system should provide not only a prediction of the article quality but also the uncertainty over its predictions. This motivates us to measure the uncertainty in document quality predictions, in addition to making the label prediction. Experimental results show that both Gaussian processes (GPs) and random forests (RFs) can yield competitive results in predicting the quality of Wikipedia articles, while providing an estimate of uncertainty when there is inconsistency in the quality labels from the Wikipedia contributors. We additionally evaluate our methods in the context of a semi-automated document quality class assignment decision-making process, where there is asymmetric risk associated with overestimates and underestimates of document quality. Our experiments suggest that GPs provide more reliable estimates in this context.

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On the Role of Scene Graphs in Image Captioning
Dalin Wang | Daniel Beck | Trevor Cohn
Proceedings of the Beyond Vision and LANguage: inTEgrating Real-world kNowledge (LANTERN)

Scene graphs represent semantic information in images, which can help image captioning system to produce more descriptive outputs versus using only the image as context. Recent captioning approaches rely on ad-hoc approaches to obtain graphs for images. However, those graphs introduce noise and it is unclear the effect of parser errors on captioning accuracy. In this work, we investigate to what extent scene graphs can help image captioning. Our results show that a state-of-the-art scene graph parser can boost performance almost as much as the ground truth graphs, showing that the bottleneck currently resides more on the captioning models than on the performance of the scene graph parser.

2018

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Graph-to-Sequence Learning using Gated Graph Neural Networks
Daniel Beck | Gholamreza Haffari | Trevor Cohn
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Many NLP applications can be framed as a graph-to-sequence learning problem. Previous work proposing neural architectures on graph-to-sequence obtained promising results compared to grammar-based approaches but still rely on linearisation heuristics and/or standard recurrent networks to achieve the best performance. In this work propose a new model that encodes the full structural information contained in the graph. Our architecture couples the recently proposed Gated Graph Neural Networks with an input transformation that allows nodes and edges to have their own hidden representations, while tackling the parameter explosion problem present in previous work. Experimental results shows that our model outperforms strong baselines in generation from AMR graphs and syntax-based neural machine translation.

2017

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Learning Kernels over Strings using Gaussian Processes
Daniel Beck | Trevor Cohn
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Non-contiguous word sequences are widely known to be important in modelling natural language. However they not explicitly encoded in common text representations. In this work we propose a model for text processing using string kernels, capable of flexibly representing non-contiguous sequences. Specifically, we derive a vectorised version of the string kernel algorithm and their gradients, allowing efficient hyperparameter optimisation as part of a Gaussian Process framework. Experiments on synthetic data and text regression for emotion analysis show the promise of this technique.

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Modelling Representation Noise in Emotion Analysis using Gaussian Processes
Daniel Beck
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Emotion Analysis is the task of modelling latent emotions present in natural language. Labelled datasets for this task are scarce so learning good input text representations is not trivial. Using averaged word embeddings is a simple way to leverage unlabelled corpora to build text representations but this approach can be prone to noise either coming from the embedding themselves or the averaging procedure. In this paper we propose a model for Emotion Analysis using Gaussian Processes and kernels that are better suitable for functions that exhibit noisy behaviour. Empirical evaluations in a emotion prediction task show that our model outperforms commonly used baselines for regression.

2016

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Exploring Prediction Uncertainty in Machine Translation Quality Estimation
Daniel Beck | Lucia Specia | Trevor Cohn
Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning

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SHEF-MIME: Word-level Quality Estimation Using Imitation Learning
Daniel Beck | Andreas Vlachos | Gustavo Paetzold | Lucia Specia
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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Word embeddings and discourse information for Quality Estimation
Carolina Scarton | Daniel Beck | Kashif Shah | Karin Sim Smith | Lucia Specia
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers

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USFD at SemEval-2016 Task 1: Putting different State-of-the-Arts into a Box
Ahmet Aker | Frederic Blain | Andres Duque | Marina Fomicheva | Jurica Seva | Kashif Shah | Daniel Beck
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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Speed-Constrained Tuning for Statistical Machine Translation Using Bayesian Optimization
Daniel Beck | Adrià de Gispert | Gonzalo Iglesias | Aurelien Waite | Bill Byrne
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2015

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Learning Structural Kernels for Natural Language Processing
Daniel Beck | Trevor Cohn | Christian Hardmeier | Lucia Specia
Transactions of the Association for Computational Linguistics, Volume 3

Structural kernels are a flexible learning paradigm that has been widely used in Natural Language Processing. However, the problem of model selection in kernel-based methods is usually overlooked. Previous approaches mostly rely on setting default values for kernel hyperparameters or using grid search, which is slow and coarse-grained. In contrast, Bayesian methods allow efficient model selection by maximizing the evidence on the training data through gradient-based methods. In this paper we show how to perform this in the context of structural kernels by using Gaussian Processes. Experimental results on tree kernels show that this procedure results in better prediction performance compared to hyperparameter optimization via grid search. The framework proposed in this paper can be adapted to other structures besides trees, e.g., strings and graphs, thereby extending the utility of kernel-based methods.

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SHEF-NN: Translation Quality Estimation with Neural Networks
Kashif Shah | Varvara Logacheva | Gustavo Paetzold | Frederic Blain | Daniel Beck | Fethi Bougares | Lucia Specia
Proceedings of the Tenth Workshop on Statistical Machine Translation

2014

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SHEF-Lite 2.0: Sparse Multi-task Gaussian Processes for Translation Quality Estimation
Daniel Beck | Kashif Shah | Lucia Specia
Proceedings of the Ninth Workshop on Statistical Machine Translation

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Joint Emotion Analysis via Multi-task Gaussian Processes
Daniel Beck | Trevor Cohn | Lucia Specia
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Bayesian Kernel Methods for Natural Language Processing
Daniel Beck
Proceedings of the ACL 2014 Student Research Workshop

2013

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Reducing Annotation Effort for Quality Estimation via Active Learning
Daniel Beck | Lucia Specia | Trevor Cohn
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

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SHEF-Lite: When Less is More for Translation Quality Estimation
Daniel Beck | Kashif Shah | Trevor Cohn | Lucia Specia
Proceedings of the Eighth Workshop on Statistical Machine Translation

2011

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Syntax-based Statistical Machine Translation using Tree Automata and Tree Transducers
Daniel Emilio Beck
Proceedings of the ACL 2011 Student Session

2006

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Automatic Testing and Evaluation of Multilingual Language Technology Resources and Components
Ulrich Schäfer | Daniel Beck
Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)

We describe SProUTomat, a tool for daily building, testing and evaluating a complex general-purpose multilingual natural language text processor including its linguistic resources (lingware). Software and lingware are developed, maintained and extended in a distributed manner by multiple authors and projects, i.e., the source code stored in a version control system is modified frequently. The modular design of different, dedicated lingware modules like tokenizers, morphology, gazetteers, type hierarchy, rule formalism on the one hand increases flexibility and re-usability, but on the other hand may lead to fragility with respect to changes. Therefore, frequent testing as known from software engineering is necessary also for lingware to warrant a high level of quality and overall stability of the system. We describe the build, testing and evaluation methods for LT software and lingware we have developed on the basis of the open source, platform-independent Apache Ant tool and the configurable evaluation tool JTaCo.