Taiwan

We present a novel approach for text sanitization, which is the task of editing a document to mask all (direct and indirect) personal identifiers and thereby conceal the identity of the individuals(s) mentioned in the text. In contrast to previous work, the approach relies on explicit measures of privacy risk, making it possible to explicitly control the trade-off between privacy protection and data utility. 

The approach proceeds in three steps. A neural, privacy-enhanced entity recognizer is first employed to detect and classify potential personal identifiers. We then determine which entities, or combination of entities, are likely to pose a re-identification risk through a range of privacy risk assessment measures. We present three such measures of privacy risk, respectively based on (1) span probabilities derived from a BERT language model, (2) web search queries and (3) a classifier trained on labelled data. Finally, a linear optimization solver decides which entities to mask to minimize the semantic loss while simultaneously ensuring that the estimated privacy risk remains under a given threshold. We evaluate the approach both in the absence and presence of manually annotated data. Our results highlight the potential of the approach, as well as issues specific types of personal data can introduce to the process.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.18/)

IJCNLP-AACL 2022

Neural Text Sanitization with Explicit Measures of Privacy Risk

data privacy

privacy protection

language models

[![](https://assets.underline.io/uploads/markdown_image/1/image/81a3c0317d24f663b49b996875024d45.png)](https://aclanthology.org/volumes/2022.coling-1/)



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COLING 2022

COLING, the International Conference on Computational Linguistics, is one of the premier conferences for the natural language processing and computational linguistics.

technical paper

The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP).

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The AACL, Asia-Pacific Chapter of the Association for Computational Linguistics, and the AFNLP, Asian Federation of Natural Language Processing, invite you to participate in the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the 10th International Joint Conference on Natural Language Processing (IJCNLP). 

Many NLP main tasks benefit from an accurate understanding of temporal expressions, e.g., text summarization, question answering, and information retrieval. This paper introduces Hengam, an adversarially trained transformer for Persian temporal tagging outperforming state-of-the-art approaches on a diverse and manually created dataset. We create Hengam in the following concrete steps: (1) we develop HengamTagger, an extensible rule-based tool that can extract temporal expressions from a set of diverse language-specific patterns for any language of interest. (2) We apply HengamTagger to annotate temporal tags in a large and diverse Persian text collection (covering both formal and informal contexts) to be used as weakly labeled data. (3) We introduce an adversarially trained transformer model on HengamCorpus that can generalize over the HengamTagger’s rules. We create HengamGold, the first high-quality gold standard for Persian temporal tagging. Our trained adversarial HengamTransformer not only achieves the best performance in terms of the F1-score (a type F1-Score of 95.42 and a partial F1-Score of 91.60) but also successfully deals with language ambiguities and incorrect spellings. Our code, data, and models are publicly available at https://github.com/kargaranamir/Hengam.

Hengam: An Adversarially Trained Transformer for Persian Temporal Tagging

IR models using a pretrained language model significantly outperform lexical approaches like BM25. In particular, SPLADE, which encodes texts to sparse vectors, is an effective model for practical use because it shows robustness to out-of-domain datasets. However, SPLADE still struggles with exact matching of low-frequency words in training data. In addition, domain shifts in vocabulary and word frequencies deteriorate the IR performance of SPLADE. Because supervision data are scarce in the target domain, addressing the domain shifts without supervision data is necessary.
This paper proposes an unsupervised domain adaptation method by filling vocabulary and word-frequency gaps. First, we expand a vocabulary and execute continual pretraining with a masked language model on a corpus of the target domain. Then, we multiply SPLADE-encoded sparse vectors by inverse document frequency weights to consider the importance of documents with low-frequency words. 
We conducted experiments using our method on datasets with a large vocabulary gap from a source domain. We show that our method outperforms the present state-of-the-art domain adaptation method. In addition, our method achieves state-of-the-art results, combined with BM25.

Unsupervised Domain Adaptation for Sparse Retrieval by Filling Vocabulary and Word Frequency Gaps

Sentence-level relation extraction (RE) aims at identifying the relationship between two entities in a sentence. Many efforts have been devoted to this problem, while the best performing methods are still far from perfect. In this paper, we revisit two problems that affect the performance of existing RE models, namely entity representation and noisy or ill-defined labels. Our improved RE baseline, incorporated with entity representations with typed markers, achieves an F1 of 74.6% on TACRED, significantly outperforms previous SOTA methods. Furthermore, the presented new baseline achieves an F1 of 91.1% on the refined Re-TACRED dataset, demonstrating that the pretrained language models (PLMs) achieve high performance on this task. We release our code to the community for future research.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.21/)

An Improved Baseline for Sentence-level Relation Extraction

Despite recent progress in video and language representation learning, the weak or sparse correspondence between the two modalities remains a bottleneck in the area. Most video-language models are trained via pair-level loss to predict whether a pair of video and text is aligned. However, even in paired video-text segments, only a subset of the frames are semantically relevant to the corresponding text, with the remainder representing noise; where the ratio of noisy frames is higher for longer videos. We propose FineCo (Fine-grained Contrastive Loss for Frame Sampling), an approach to better learn video and language representations with a fine-grained contrastive objective operating on video frames. It helps distil a video by selecting the frames that are semantically equivalent to the text, improving cross-modal correspondence. Building on the well established VideoCLIP model as a starting point, FineCo achieves state-of-the-art performance on YouCookII, a text-video retrieval benchmark with long videos. FineCo also achieves competitive results on text-video retrieval (MSR-VTT), and video question answering datasets (MSR-VTT QA and MSR-VTT MC) with shorter videos.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.53/)

Contrastive Video-Language Learning with Fine-grained Frame Sampling

Linguistic disparity in the NLP world is a problem that has been widely acknowledged recently. However, different facets of this problem, or the reasons behind this disparity are seldom discussed within the NLP community. This paper provides a comprehensive analysis of the disparity that exists within the languages of the world. We show that simply categorising languages considering data availability may not be always correct. Using an existing language categorisation based on speaker population and vitality, we analyse the distribution of language data resources, amount of NLP/CL research, inclusion in multilingual web-based platforms and the inclusion in pre-trained multilingual models. We show that many languages do not get covered in these resources or platforms, and even within the languages belonging to the same language group, there is wide disparity. We analyse the impact of family, geographical location, GDP and the speaker population of languages and provide possible reasons for this disparity, along with some suggestions to overcome the same.


Some Languages are More Equal than Others: Probing Deeper into the Linguistic Disparity in the NLP World

Text autoencoders are often used for unsupervised conditional text generation by applying mappings in the latent space to change attributes to the desired values.
Recently, Mai et al. (2020) proposed Emb2Emb, a method to learn these mappings in the embedding space of an autoencoder. 
However, their method is restricted to autoencoders with a single-vector embedding, which limits how much information can be retained. 
We address this issue by extending their method to Bag-of-Vectors Autoencoders (BoV-AEs), which encode the text into a variable-size bag of vectors that grows with the size of the text, as in attention-based models. 
This allows to encode and reconstruct much longer texts than standard autoencoders.
Analogous to conventional autoencoders, we propose regularization techniques that facilitate learning meaningful operations in the latent space. 
Finally, we adapt Emb2Emb for a training scheme that learns to map an input bag to an output bag, including a novel loss function and neural architecture.
Our empirical evaluations on unsupervised sentiment transfer show that our method performs substantially better than a standard autoencoder.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.36/)

Bag-of-Vectors Autoencoders for Unsupervised Conditional Text Generation

Adapter-based tuning, by adding light-weight adapters to multilingual pretrained language models (mPLMs), selectively updates language-specific parameters to adapt to a new language, instead of finetuning all shared weights. This paper explores an effective way to leverage a public pool of pretrained language adapters, to overcome resource imbalances for low-resource languages (LRLs). Specifically,
our research questions are, whether pretrained adapters can be composed, to complement or replace LRL adapters. While composing adapters for multi-task learning setting has been studied, the same question for LRLs has remained largely unanswered. To answer this question, we study how to fuse adapters across languages and tasks, then validate how our proposed fusion adapter, namely FAD-X, can enhance a cross-lingual transfer from pretrained adapters, for well-known named entity recognition and classification benchmarks.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.8/)

FAD-X: Fusing Adapters for Cross-lingual Transfer to Low-Resource Languages

Despite the promising evaluation results by knowledge distillation (KD) in natural language understanding (NLU) and sequence-to-sequence (seq2seq) tasks, KD for causal language modeling (LM) remains a challenge. In this paper, we study KD for causal LM and propose plug and play knowledge distillation (PP-KD) for the kNN-LM that is the state-of-the-art in causal language modeling, by leveraging external logits from either a powerful or a heterogeneous teacher LM. Unlike conventional logit-based KD where the teacher's knowledge is built in during training, PP-KD is plug and play: it stores the teacher's knowledge (i.e., logits) externally and uses the teacher's logits of the retrieved k-nearest neighbors during kNN-LM inference at test time. In contrast to marginal perplexity improvement by logit-based KD in conventional neural (causal) LM, PP-KD achieves a significant improvement enhancing the kNN-LMs in multiple language modeling datasets, showing a novel and promising perspective for causal LM distillation.

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.57/)

Plug and Play Knowledge Distillation for kNN-LM with External Logits

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-short.7/)

Robust Hate Speech Detection via Mitigating Spurious Correlations

Recent progress in language model pre-training has led to important improvements in Named Entity Recognition (NER). Nonetheless, this progress has been mainly tested in well-formatted documents such as news, Wikipedia, or scientific articles. In social media the landscape is different, in which it adds another layer of complexity due to its noisy and dynamic nature. In this paper, we focus on NER in Twitter, one of the largest social media platforms, and construct a new NER dataset, TweetNER7, which contains seven entity types annotated over 11,382 tweets from September 2019 to August 2021. The dataset was constructed by carefully distributing the tweets over time and taking representative trends as a basis. Along with the dataset, we provide a set of language model baselines and perform an analysis on the language model performance on the task, especially analyzing the impact of different time periods. In particular, we focus on three important temporal aspects in our analysis: short-term degradation of NER models over time, strategies to fine-tune a language model over different periods, and self-labeling as an alternative to lack of recently-labeled data. TweetNER7 is released publicly (this https URL) along with the models fine-tuned on it (NER models have been integrated into TweetNLP and can be found athttps://github.com/asahi417/tner/tree/master/examples/tweetner7_paper).

To view the proceedings, please [click here](https://aclanthology.org/2022.aacl-main.25/)

Neural Text Sanitization with Explicit Measures of Privacy Risk

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