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State of the art end-to-end coreference resolution models use expensive span representations and antecedent prediction mechanisms. These approaches are expensive both in terms of their memory requirements as well as compute time, and are particularly ill-suited for long documents. In this paper, we propose an approximation to end-to-end models which scales gracefully to documents of any length. Replacing span representations with token representations, we reduce the time/memory complexity via token windows and nearest neighbor sparsification methods for more efficient antecedent prediction. We show our approach&#39;s resulting reduction of training and inference time compared to state-of-the-art methods with only a minimal loss in accuracy.

IJCNLP-AACL 2021

Scaling Within Document Coreference to Long Texts

scalable

coreference

long documents

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


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The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

State of the art end-to-end coreference resolution models use expensive span representations and antecedent prediction mechanisms. These approaches are expensive both in terms of their memory requirements as well as compute time, and are particularly ill-suited for long documents. In this paper, we propose an approximation to end-to-end models which scales gracefully to documents of any length. Replacing span representations with token representations, we reduce the time/memory complexity via token windows and nearest neighbor sparsification methods for more efficient antecedent prediction. We show our approach's resulting reduction of training and inference time compared to state-of-the-art methods with only a minimal loss in accuracy.

technical paper

Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. The ability to accurately and efficiently predict mood from easily collectible data has several important implications for the early detection, intervention, and treatment of mental health disorders. One promising data source to help monitor human behavior is daily smartphone usage. However, care must be taken to summarize behaviors without identifying the user through personal (e.g., personally identifiable information) or protected (e.g., race, gender) attributes. In this paper, we study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors. Using computational models, we find that language and multimodal representations of mobile typed text (spanning typed characters, words, keystroke timings, and app usage) are predictive of daily mood. However, we find that models trained to predict mood often also capture private user identities in their intermediate representations. To tackle this problem, we evaluate approaches that obfuscate user identity while remaining predictive. By combining multimodal representations with privacy-preserving learning, we are able to push forward the performance-privacy frontier.

Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data

This position paper investigates the problem of automated text anonymisation, which is a prerequisite for secure sharing of documents containing sensitive information about individuals.
We summarise the key concepts behind text anonymisation and provide a review of current approaches. Anonymisation methods have so far been developed in two fields with little mutual interaction, namely natural language processing and privacy-preserving data publishing. Based on a case study, we outline the benefits and limitations of these approaches and discuss a number of open challenges, such as (1) how to account for multiple types of semantic inferences, (2) how to strike a balance between disclosure risk and data utility and (3) how to evaluate the quality of the resulting anonymisation. We lay out a case for moving beyond sequence labelling models and incorporate explicit measures of disclosure risk into the text anonymisation process.

Anonymisation Models for Text Data: State of the art, Challenges and Future Directions

Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks such as WMT. However, there still exist significant issues such as robustness, domain generalization, etc. In this paper, we study NMT models from the perspective of compositional generalization by building a benchmark dataset, CoGnition, consisting of 216k clean and consistent sentence pairs. We quantitatively analyze effects of various factors using compound translation error rate, then demonstrate that the NMT model fails badly on compositional generalization, although it performs remarkably well under traditional metrics.

On Compositional Generalization of Neural Machine Translation

Word alignment, which aims to align translationally equivalent words between source and target sentences, plays an important role in many natural language processing tasks. Current unsupervised neural alignment methods focus on inducing alignments from neural machine translation models, which does not leverage the full context in the target sequence. In this paper, we propose Mask-Align, a self-supervised word alignment model that takes advantage of the full context on the target side. Our model masks out each target token and predicts it conditioned on both source and the remaining target tokens. This two-step process is based on the assumption that the source token contributing most to recovering the masked target token should be aligned. We also introduce an attention variant called leaky attention, which alleviates the problem of unexpected high cross-attention weights on special tokens such as periods. Experiments on four language pairs show that our model outperforms previous unsupervised neural aligners and obtains new state-of-the-art results.

Mask-Align: Self-Supervised Neural Word Alignment

Computer-aided translation (CAT), the use of software to assist a human translator in the translation process, has been proven to be useful in enhancing the productivity of human translators. Autocompletion, which suggests translation results according to the text pieces provided by human translators, is a core function of CAT. There are two limitations in previous research in this line. First, most research works on this topic focus on sentence-level autocompletion (i.e., generating the whole translation as a sentence based on human input), but word-level autocompletion is under-explored so far. Second, almost no public benchmarks are available for the autocompletion task of CAT. This might be among the reasons why research progress in CAT is much slower compared to automatic MT. In this paper, we propose the task of general word-level autocompletion (GWLAN) from a real-world CAT scenario, and construct the first public benchmark to facilitate research in this topic. In addition, we propose an effective method for GWLAN and compare it with several strong baselines. Experiments demonstrate that our proposed method can give significantly more accurate predictions than the baseline methods on our benchmark datasets.

GWLAN: General Word-Level AutocompletioN for Computer-Aided Translation

Recent neural dialogue models have achieved high performance in response ranking and evaluation in open-domain settings. Response ranking and evaluation tasks are formulated as context-response binary classification tasks and models generally learn to make predictions based on context-response content similarity. However, over-reliance on content similarity makes the models less sensitive to the presence of inconsistencies, incorrect time expressions and other factors important for response appropriateness and coherence. We propose approaches for automatically creating adversarial negative training data to help ranking and evaluation models learn features beyond content similarity. We propose mask-and-fill and keyword-guided approaches that generate negative examples for training more robust dialogue systems. These are adversarial responses that have high content similarity with the contexts but are either incoherent, inappropriate or not fluent. Our approaches are fully data-driven and can be easily incorporated with existing models and datasets. Experiments on classification, ranking and evaluation tasks across multiple datasets demonstrate that our approaches outperform strong baselines in providing informative negative examples for training dialogue systems.

Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation


With the rapid growth of social media in the past decade, the news are no longer controlled by just a few mainstream sources. Users themselves create large numbers of potentially fictitious rumours, necessitating automated veracity classification systems. Here we present a novel approach towards automatically classifying rumours circulating on Twitter with respect to their veracity. We use a model built on Variational Autoencoder which disentangles the informational content of a tweet from the manner in which the information is written. This is achieved by obtaining latent topic vectors in an adversarial learning setting using the auxiliary task of stance classification. The latent vectors learnt in this way are used to predict rumour veracity, obtaining state-of-the-art accuracy scores on the PHEME dataset.

Learning Disentangled Latent Topics for Twitter Rumour Veracity Classification

We introduce a novel dataset of human judgments of machine-edited text and initial models of those perceptions. Six machine-editing methods ranging from character swapping to variational autoencoders are applied to collections of English-language social media text and scientific abstracts. The edits are judged in context for detectability and the extent to which they preserve the meaning of the original. Automated measures of semantic similarity and fluency are evaluated individually and combined to produce composite models of human perception. Both meaning preservation and detectability are predicted within 6% of the upper bound of human consensus labeling.

Perceptual Models of Machine-Edited Text

Word embedding techniques depend heavily on the frequencies of words in the corpus, and are negatively impacted by failures in providing reliable representations for low-frequency words or unseen words during training. To address this problem, we propose an algorithm to learn embeddings for rare words based on an Internet search engine and the spatial location relationships. Our algorithm proceeds in two steps. We firstly retrieve webpages corresponding to the rare word through the search engine and parse the returned results to extract a set of most related words. We average the vectors of the related words as the initial vector of the rare word. Then, the location of the rare word in the vector space is iteratively fine-tuned according to the order of its relevances to the related words. Compared to other approaches, our algorithm can learn more accurate representations for a wider range of vocabulary. We evaluate our learned rare-word embeddings on the word relatedness task, and the experimental results show that our algorithm achieves state-of-the-art performance.

Learning Embeddings for Rare Words Leveraging Internet Search Engine and Spatial Location Relationships

Cross-lingual representations have the potential to make NLP techniques available to the vast majority of languages in the world. However, they currently require large pretraining corpora or access to typologically similar languages. In this work, we address these obstacles by removing language identity signals from multilingual embeddings. We examine three approaches for this: (i) re-aligning the vector spaces of target languages (all together) to a pivot source language; (ii) removing language-specific means and variances, which yields better discriminativeness of embeddings as a by-product; and (iii) increasing input similarity across languages by removing morphological contractions and sentence reordering. We evaluate on XNLI and reference-free MT across 19 typologically diverse languages. Our findings expose the limitations of these approaches -- unlike vector normalization, vector space re-alignment and text normalization do not achieve consistent gains across encoders and languages. Due to the approaches' additive effects, their combination decreases the cross-lingual transfer gap by 8.9 points (m-BERT) and 18.2 points (XLM-R) on average across all tasks and languages, however. Our code and models are publicly available.

Scaling Within Document Coreference to Long Texts

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Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data

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Scaling Within Document Coreference to Long Texts

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Next from IJCNLP-AACL 2021

Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data

Similar lecture

Intrinsic Bias Metrics Do Not Correlate with Application Bias

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads