Wray Buntine


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On the Effect of Isotropy on VAE Representations of Text
Lan Zhang | Wray Buntine | Ehsan Shareghi
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Injecting desired geometric properties into text representations has attracted a lot of attention. A property that has been argued for, due to its better utilisation of representation space, is isotropy. In parallel, VAEs have been successful in areas of NLP, but are known for their sub-optimal utilisation of the representation space. To address an aspect of this, we investigate the impact of injecting isotropy during training of VAEs. We achieve this by using an isotropic Gaussian posterior (IGP) instead of the ellipsoidal Gaussian posterior. We illustrate that IGP effectively encourages isotropy in the representations, inducing a more discriminative latent space. Compared to vanilla VAE, this translates into a much better classification performance, robustness to input perturbation, and generative behavior. Additionally, we offer insights about the representational properties encouraged by IGP.


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Curriculum Learning Effectively Improves Low Data VQA
Narjes Askarian | Ehsan Abbasnejad | Ingrid Zukerman | Wray Buntine | Gholamreza Haffari
Proceedings of the The 19th Annual Workshop of the Australasian Language Technology Association

Visual question answering (VQA) models, in particular modular ones, are commonly trained on large-scale datasets to achieve state-of-the-art performance. However, such datasets are sometimes not available. Further, it has been shown that training these models on small datasets significantly reduces their accuracy. In this paper, we propose curriculum-based learning (CL) regime to increase the accuracy of VQA models trained on small datasets. Specifically, we offer three criteria to rank the samples in these datasets and propose a training strategy for each criterion. Our results show that, for small datasets, our CL approach yields more accurate results than those obtained when training with no curriculum.

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Neural Attention-Aware Hierarchical Topic Model
Yuan Jin | He Zhao | Ming Liu | Lan Du | Wray Buntine
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Neural topic models (NTMs) apply deep neural networks to topic modelling. Despite their success, NTMs generally ignore two important aspects: (1) only document-level word count information is utilized for the training, while more fine-grained sentence-level information is ignored, and (2) external semantic knowledge regarding documents, sentences and words are not exploited for the training. To address these issues, we propose a variational autoencoder (VAE) NTM model that jointly reconstructs the sentence and document word counts using combinations of bag-of-words (BoW) topical embeddings and pre-trained semantic embeddings. The pre-trained embeddings are first transformed into a common latent topical space to align their semantics with the BoW embeddings. Our model also features hierarchical KL divergence to leverage embeddings of each document to regularize those of their sentences, paying more attention to semantically relevant sentences. Both quantitative and qualitative experiments have shown the efficacy of our model in 1) lowering the reconstruction errors at both the sentence and document levels, and 2) discovering more coherent topics from real-world datasets.

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Multilingual Neural Machine Translation: Can Linguistic Hierarchies Help?
Fahimeh Saleh | Wray Buntine | Gholamreza Haffari | Lan Du
Findings of the Association for Computational Linguistics: EMNLP 2021

Multilingual Neural Machine Translation (MNMT) trains a single NMT model that supports translation between multiple languages, rather than training separate models for different languages. Learning a single model can enhance the low-resource translation by leveraging data from multiple languages. However, the performance of an MNMT model is highly dependent on the type of languages used in training, as transferring knowledge from a diverse set of languages degrades the translation performance due to negative transfer. In this paper, we propose a Hierarchical Knowledge Distillation (HKD) approach for MNMT which capitalises on language groups generated according to typological features and phylogeny of languages to overcome the issue of negative transfer. HKD generates a set of multilingual teacher-assistant models via a selective knowledge distillation mechanism based on the language groups, and then distills the ultimate multilingual model from those assistants in an adaptive way. Experimental results derived from the TED dataset with 53 languages demonstrate the effectiveness of our approach in avoiding the negative transfer effect in MNMT, leading to an improved translation performance (about 1 BLEU score in average) compared to strong baselines.

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Transformer over Pre-trained Transformer for Neural Text Segmentation with Enhanced Topic Coherence
Kelvin Lo | Yuan Jin | Weicong Tan | Ming Liu | Lan Du | Wray Buntine
Findings of the Association for Computational Linguistics: EMNLP 2021

This paper proposes a transformer over transformer framework, called Transformerˆ2, to perform neural text segmentation. It consists of two components: bottom-level sentence encoders using pre-trained transformers, and an upper-level transformer-based segmentation model based on the sentence embeddings. The bottom-level component transfers the pre-trained knowledge learnt from large external corpora under both single and pair-wise supervised NLP tasks to model the sentence embeddings for the documents. Given the sentence embeddings, the upper-level transformer is trained to recover the segmentation boundaries as well as the topic labels of each sentence. Equipped with a multi-task loss and the pre-trained knowledge, Transformerˆ2 can better capture the semantic coherence within the same segments. Our experiments show that (1) Transformerˆ2$manages to surpass state-of-the-art text segmentation models in terms of a commonly-used semantic coherence measure; (2) in most cases, both single and pair-wise pre-trained knowledge contribute to the model performance; (3) bottom-level sentence encoders pre-trained on specific languages yield better performance than those pre-trained on specific domains.

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Topic Model or Topic Twaddle? Re-evaluating Semantic Interpretability Measures
Caitlin Doogan | Wray Buntine
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

When developing topic models, a critical question that should be asked is: How well will this model work in an applied setting? Because standard performance evaluation of topic interpretability uses automated measures modeled on human evaluation tests that are dissimilar to applied usage, these models’ generalizability remains in question. In this paper, we probe the issue of validity in topic model evaluation and assess how informative coherence measures are for specialized collections used in an applied setting. Informed by the literature, we propose four understandings of interpretability. We evaluate these using a novel experimental framework reflective of varied applied settings, including human evaluations using open labeling, typical of applied research. These evaluations show that for some specialized collections, standard coherence measures may not inform the most appropriate topic model or the optimal number of topics, and current interpretability performance validation methods are challenged as a means to confirm model quality in the absence of ground truth data.


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Collective Wisdom: Improving Low-resource Neural Machine Translation using Adaptive Knowledge Distillation
Fahimeh Saleh | Wray Buntine | Gholamreza Haffari
Proceedings of the 28th International Conference on Computational Linguistics

Scarcity of parallel sentence-pairs poses a significant hurdle for training high-quality Neural Machine Translation (NMT) models in bilingually low-resource scenarios. A standard approach is transfer learning, which involves taking a model trained on a high-resource language-pair and fine-tuning it on the data of the low-resource MT condition of interest. However, it is not clear generally which high-resource language-pair offers the best transfer learning for the target MT setting. Furthermore, different transferred models may have complementary semantic and/or syntactic strengths, hence using only one model may be sub-optimal. In this paper, we tackle this problem using knowledge distillation, where we propose to distill the knowledge of ensemble of teacher models to a single student model. As the quality of these teacher models varies, we propose an effective adaptive knowledge distillation approach to dynamically adjust the contribution of the teacher models during the distillation process. Experiments on transferring from a collection of six language pairs from IWSLT to five low-resource language-pairs from TED Talks demonstrate the effectiveness of our approach, achieving up to +0.9 BLEU score improvements compared to strong baselines.


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Leveraging Meta Information in Short Text Aggregation
He Zhao | Lan Du | Guanfeng Liu | Wray Buntine
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Short texts such as tweets often contain insufficient word co-occurrence information for training conventional topic models. To deal with the insufficiency, we propose a generative model that aggregates short texts into clusters by leveraging the associated meta information. Our model can generate more interpretable topics as well as document clusters. We develop an effective Gibbs sampling algorithm favoured by the fully local conjugacy in the model. Extensive experiments demonstrate that our model achieves better performance in terms of document clustering and topic coherence.


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Learning How to Actively Learn: A Deep Imitation Learning Approach
Ming Liu | Wray Buntine | Gholamreza Haffari
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Heuristic-based active learning (AL) methods are limited when the data distribution of the underlying learning problems vary. We introduce a method that learns an AL “policy” using “imitation learning” (IL). Our IL-based approach makes use of an efficient and effective “algorithmic expert”, which provides the policy learner with good actions in the encountered AL situations. The AL strategy is then learned with a feedforward network, mapping situations to most informative query datapoints. We evaluate our method on two different tasks: text classification and named entity recognition. Experimental results show that our IL-based AL strategy is more effective than strong previous methods using heuristics and reinforcement learning.

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Adaptive Knowledge Sharing in Multi-Task Learning: Improving Low-Resource Neural Machine Translation
Poorya Zaremoodi | Wray Buntine | Gholamreza Haffari
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Neural Machine Translation (NMT) is notorious for its need for large amounts of bilingual data. An effective approach to compensate for this requirement is Multi-Task Learning (MTL) to leverage different linguistic resources as a source of inductive bias. Current MTL architectures are based on the Seq2Seq transduction, and (partially) share different components of the models among the tasks. However, this MTL approach often suffers from task interference and is not able to fully capture commonalities among subsets of tasks. We address this issue by extending the recurrent units with multiple “blocks” along with a trainable “routing network”. The routing network enables adaptive collaboration by dynamic sharing of blocks conditioned on the task at hand, input, and model state. Empirical evaluation of two low-resource translation tasks, English to Vietnamese and Farsi, show +1 BLEU score improvements compared to strong baselines.

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Learning to Actively Learn Neural Machine Translation
Ming Liu | Wray Buntine | Gholamreza Haffari
Proceedings of the 22nd Conference on Computational Natural Language Learning

Traditional active learning (AL) methods for machine translation (MT) rely on heuristics. However, these heuristics are limited when the characteristics of the MT problem change due to e.g. the language pair or the amount of the initial bitext. In this paper, we present a framework to learn sentence selection strategies for neural MT. We train the AL query strategy using a high-resource language-pair based on AL simulations, and then transfer it to the low-resource language-pair of interest. The learned query strategy capitalizes on the shared characteristics between the language pairs to make an effective use of the AL budget. Our experiments on three language-pairs confirms that our method is more effective than strong heuristic-based methods in various conditions, including cold-start and warm-start as well as small and extremely small data conditions.


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Leveraging Linguistic Resources for Improving Neural Text Classification
Ming Liu | Gholamreza Haffari | Wray Buntine | Michelle Ananda-Rajah
Proceedings of the Australasian Language Technology Association Workshop 2017


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Learning cascaded latent variable models for biomedical text classification
Ming Liu | Gholamreza Haffari | Wray Buntine
Proceedings of the Australasian Language Technology Association Workshop 2016


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Exploring Temporal Patterns in Emergency Department Triage Notes with Topic Models
Simon Kocbek | Karin Verspoor | Wray Buntine
Proceedings of the Australasian Language Technology Association Workshop 2014


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Topic Segmentation with a Structured Topic Model
Lan Du | Wray Buntine | Mark Johnson
Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


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Modelling Sequential Text with an Adaptive Topic Model
Lan Du | Wray Buntine | Huidong Jin
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning


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Discovery in Text: Visualisation, Topics and Statistics
Wray Buntine
Proceedings of the Australasian Language Technology Association Workshop 2011