Papers by Daniel Beck
Neural Speech Translation using Lattice Transformations and Graph Networks (D19-53)
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| Challenge: | Existing work on end-to-end systems bypass the need for intermediate representations, but this approach is limited in practical applications. |
| Approach: | They propose a lattice-tosequence model which uses lattics as encoders and graph networks to address two problems by applying latticae transformations and a neural model. |
| Outcome: | The proposed model beats pipeline approaches while being orders of magnitude faster than previous work. |
On the (In)Effectiveness of Images for Text Classification (2021.eacl-main)
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| Challenge: | Existing studies have focused on text classification, but have shown that images do not improve NLP tasks. |
| Approach: | They focus on text classification, where images complement the text and the Wikipedia page can be in one of a number of different languages. |
| Outcome: | The proposed model trains without external pre-training, but when combined with BERT models pre-trained on large-scale external data, images contribute nothing. |
Graph-to-Sequence Learning using Gated Graph Neural Networks (P18-1)
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| Challenge: | Existing approaches to graph-to-sequence learning ignore the full graph structure, discarding key information. |
| Approach: | They propose a graph-to-sequence learning model that encodes the full graph structure and an input transformation that allows nodes and edges to have their own hidden representations. |
| Outcome: | The proposed model outperforms baselines in generation from AMR graphs and syntax-based neural machine translation while retaining the full graph structure. |
FLUKE: A Linguistically-Driven and Task-Agnostic Framework for Robustness Evaluation (2026.findings-eacl)
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Yulia Otmakhova, Thinh Hung Truong, Rahmad Mahendra, Zenan Zhai, Rongxin Zhu, Daniel Beck, Jey Han Lau
| Challenge: | FLUKE introduces controlled variations across linguistic levels and leverages large language models with human validation to generate modifications. |
| Approach: | They propose a framework for assessing model robustness through systematic minimal variations of test data. |
| Outcome: | The proposed framework evaluates models and LLMs across six diverse NLP tasks and shows that they are more robust to natural, fluent modifications than base models. |
Performance Prediction via Bayesian Matrix Factorisation for Multilingual Natural Language Processing Tasks (2023.eacl-main)
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| Challenge: | Performance prediction for natural language processing (NLP) is based on a framework of Bayesian matrix factorisation . it avoids hyperparameter tuning and provides uncertainty estimates over predictions. |
| Approach: | They propose to use Bayesian matrix factorisation to predict the performance of language pairs depicted by grey cells. |
| Outcome: | The proposed framework outperforms the state-of-the-art in several NLP benchmarks, including machine translation and cross-lingual entity linking. |
Modelling Uncertainty in Collaborative Document Quality Assessment (D19-55)
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| Challenge: | Existing work on document quality assessment relies on predicting the quality of a document relative to a putative gold standard, without paying attention to the subjectivity of this task. |
| Approach: | They propose to use Gaussian processes and random forests to measure the uncertainty in document quality predictions. |
| Outcome: | The proposed methods can predict the quality of Wikipedia articles while providing an estimate of uncertainty when there is inconsistency in the quality labels from the contributors. |
Modeling Emotion Dynamics in Song Lyrics with State Space Models (2023.tacl-1)
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| Challenge: | Existing studies in music emotion recognition assume a single label for the whole song, but annotated data is scarce and difficult to obtain. |
| Approach: | They propose a method to predict emotion dynamics in song lyrics without annotation . they frame each song as a time series and use a State Space Model to generate the full emotion dynamics. |
| Outcome: | The proposed method improves performance of sentence-level baselines without annotating songs, making it ideal for limited training scenarios. |
Uncertainty Estimation and Reduction of Pre-trained Models for Text Regression (2022.tacl-1)
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| Challenge: | State-of-the-art classification and regression models are often not well calibrated and can be inaccurate. |
| Approach: | They quantify calibration of pre- trained language models for text regression . they apply uncertainty estimates to augment training data in low-resource domains . |
| Outcome: | The proposed model calibrations improve performance and generalizability in low-resource settings. |
On the Role of Scene Graphs in Image Captioning (D19-64)
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| Challenge: | Recent captioning approaches rely on ad-hoc approaches to obtain graphs for images, but they introduce noise and it is unclear the effect of parser errors on captioning accuracy. |
| Approach: | They investigate whether scene graphs can help image captioning . they show that a scene graph parser can boost performance almost as much as ground truth graphs . |
| Outcome: | The proposed parser can boost performance almost as much as ground truth graphs . |