pwc-archive/papers-with-abstracts huggingface dataset:
Machine Translation for Livonian: Catering to 20 Speakers
Authors: Matīss Rikters, Marili Tomingas, Tuuli Tuisk, Valts Ernštreits, Mark Fishel
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Cross-Lingual Transfer, Low Resource NMT, Machine Translation, NMT, Translation

Livonian is one of the most endangered languages in Europe with just a tiny handful of speakers and virtually no publicly available corpora. In this paper we tackle the task of developing neural machine translation (NMT) between Livonian and English, with a two-fold aim: on one hand, preserving the language and on the other – enabling access to Livonian folklore, lifestories and other textual intangible heritage as well as making it easier to create further parallel corpora. We rely on Livonian’s linguistic similarity to Estonian and Latvian and collect parallel and monolingual data for the four languages for translation experiments. We combine different low-resource NMT techniques like zero-shot translation, cross-lingual transfer and synthetic data creation to reach the highest possible translation quality as well as to find which base languages are empirically more helpful for transfer to Livonian. The resulting NMT systems and the collected monolingual and parallel data, including a manually translated and verified translation benchmark, are publicly released via OPUS and Huggingface repositories.
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based Games
Authors: Dongwon Ryu, Ehsan Shareghi, Meng Fang, Yunqiu Xu, Shirui Pan, Reza Haf
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Deep Reinforcement Learning, Efficient Exploration, Inductive Bias, Language Modeling, Language Modelling, text-based games

Text-based games (TGs) are exciting testbeds for developing deep reinforcement learning techniques due to their partially observed environments and large action spaces. In these games, the agent learns to explore the environment via natural language interactions with the game simulator. A fundamental challenge in TGs is the efficient exploration of the large action space when the agent has not yet acquired enough knowledge about the environment. We propose CommExpl, an exploration technique that injects external commonsense knowledge, via a pretrained language model (LM), into the agent during training when the agent is the most uncertain about its next action. Our method exhibits improvement on the collected game scores during the training in four out of nine games from Jericho. Additionally, the produced trajectory of actions exhibit lower perplexity, when tested with a pretrained LM, indicating better closeness to human language.
S^4-Tuning: A Simple Cross-lingual Sub-network Tuning Method
Authors: Runxin Xu, Fuli Luo, Baobao Chang, Songfang Huang, Fei Huang
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Transfer Learning

The emergence of multilingual pre-trained language models makes it possible to adapt to target languages with only few labeled examples.However, vanilla fine-tuning tends to achieve degenerated and unstable results, owing to the Language Interference among different languages, and Parameter Overload under the few-sample transfer learning scenarios.To address two problems elegantly, we propose S^4-Tuning, a Simple Cross-lingual Sub-network Tuning method. S^4-Tuning first detects the most essential sub-network for each target language, and only updates it during fine-tuning.In this way, the language sub-networks lower the scale of trainable parameters, and hence better suit the low-resource scenarios.Meanwhile, the commonality and characteristics across languages are modeled by the overlapping and non-overlapping parts to ease the interference among languages.Simple but effective, S^4-Tuning gains consistent improvements over vanilla fine-tuning on three multi-lingual tasks involving 37 different languages in total (XNLI, PAWS-X, and Tatoeba).
Region-dependent temperature scaling for certainty calibration and application to class-imbalanced token classification
Authors: Hillary Dawkins, Isar Nejadgholi
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: NER, token-classification, Token Classification

Certainty calibration is an important goal on the path to interpretability and trustworthy AI. Particularly in the context of human-in-the-loop systems, high-quality low to mid-range certainty estimates are essential. In the presence of a dominant high-certainty class, for instance the non-entity class in NER problems, existing calibration error measures are completely insensitive to potentially large errors in this certainty region of interest. We introduce a region-balanced calibration error metric that weights all certainty regions equally. When low and mid certainty estimates are taken into account, calibration error is typically larger than previously reported. We introduce a simple extension of temperature scaling, requiring no additional computation, that can reduce both traditional and region-balanced notions of calibration error over existing baselines.
Sequence-to-sequence AMR Parsing with Ancestor Information
Authors: Chen Yu, Daniel Gildea
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: AMR Parsing, Decoder, Sentence

AMR parsing is the task that maps a sentence to an AMR semantic graph automatically. The difficulty comes from generating the complex graph structure. The previous state-of-the-art method translates the AMR graph into a sequence, then directly fine-tunes a pretrained sequence-to-sequence Transformer model (BART). However, purely treating the graph as a sequence does not take advantage of structural information about the graph. In this paper, we design several strategies to add the important ancestor information into the Transformer Decoder. Our experiments show that we can improve the performance for both AMR 2.0 and AMR 3.0 dataset and achieve new state-of-the-art results.
DMix: Adaptive Distance-aware Interpolative Mixup
Authors: Ramit Sawhney, Megh Thakkar, Shrey Pandit, Ritesh Soun, Di Jin, Diyi Yang, Lucie Flek
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Data Augmentation, Diversity, Sentence, Sentence Classification

Interpolation-based regularisation methods such as Mixup, which generate virtual training samples, have proven to be effective for various tasks and modalities.We extend Mixup and propose DMix, an adaptive distance-aware interpolative Mixup that selects samples based on their diversity in the embedding space. DMix leverages the hyperbolic space as a similarity measure among input samples for a richer encoded representation.DMix achieves state-of-the-art results on sentence classification over existing data augmentation methods on 8 benchmark datasets across English, Arabic, Turkish, and Hindi languages while achieving benchmark F1 scores in 3 times less number of iterations.We probe the effectiveness of DMix in conjunction with various similarity measures and qualitatively analyze the different components.DMix being generalizable, can be applied to various tasks, models and modalities.
HYPHEN: Hyperbolic Hawkes Attention For Text Streams
Authors: Shivam Agarwal, Ramit Sawhney, Sanchit Ahuja, Ritesh Soun, Sudheer Chava
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Stock Price Prediction

Analyzing the temporal sequence of texts from sources such as social media, news, and parliamentary debates is a challenging problem as it exhibits time-varying scale-free properties and fine-grained timing irregularities. We propose a Hyperbolic Hawkes Attention Network (HYPHEN), which learns a data-driven hyperbolic space and models irregular powerlaw excitations using a hyperbolic Hawkes process. Through quantitative and exploratory experiments over financial NLP, suicide ideation detection, and political debate analysis we demonstrate HYPHEN’s practical applicability for modeling online text sequences in a geometry agnostic manner.
A Risk-Averse Mechanism for Suicidality Assessment on Social Media
Authors: Ramit Sawhney, Atula Neerkaje, Manas Gaur
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5

Recent studies have shown that social media has increasingly become a platform for users to express suicidal thoughts outside traditional clinical settings. With advances in Natural Language Processing strategies, it is now possible to design automated systems to assess suicide risk. However, such systems may generate uncertain predictions, leading to severe consequences. We hence reformulate suicide risk assessment as a selective prioritized prediction problem over the Columbia Suicide Severity Risk Scale (C-SSRS). We propose SASI, a risk-averse and self-aware transformer-based hierarchical attention classifier, augmented to refrain from making uncertain predictions. We show that SASI is able to refrain from 83% of incorrect predictions on real-world Reddit data. Furthermore, we discuss the qualitative, practical, and ethical aspects of SASI for suicide risk assessment as a human-in-the-loop framework.
When classifying grammatical role, BERT doesn’t care about word order... except when it matters
Authors: Isabel Papadimitriou, Richard Futrell, Kyle Mahowald
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5

Because meaning can often be inferred from lexical semantics alone, word order is often a redundant cue in natural language. For example, the words chopped, chef, and onion are more likely used to convey “The chef chopped the onion,” not “The onion chopped the chef.” Recent work has shown large language models to be surprisingly word order invariant, but crucially has largely considered natural prototypical inputs, where compositional meaning mostly matches lexical expectations. To overcome this confound, we probe grammatical role representation in English BERT and GPT-2, on instances where lexical expectations are not sufficient, and word order knowledge is necessary for correct classification. Such non-prototypical instances are naturally occurring English sentences with inanimate subjects or animate objects, or sentences where we systematically swap the arguments to make sentences like “The onion chopped the chef”. We find that, while early layer embeddings are largely lexical, word order is in fact crucial in defining the later-layer representations of words in semantically non-prototypical positions. Our experiments isolate the effect of word order on the contextualization process, and highlight how models use context in the uncommon, but critical, instances where it matters.
Focus on the Target’s Vocabulary: Masked Label Smoothing for Machine Translation
Authors: Liang Chen, Runxin Xu, Baobao Chang
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Machine Translation, Translation

Label smoothing and vocabulary sharing are two widely used techniques in neural machine translation models. However, we argue that simply applying both techniques can be conflicting and even leads to sub-optimal performance. When allocating smoothed probability, original label smoothing treats the source-side words that would never appear in the target language equally to the real target-side words, which could bias the translation model. To address this issue, we propose Masked Label Smoothing (MLS), a new mechanism that masks the soft label probability of source-side words to zero. Simple yet effective, MLS manages to better integrate label smoothing with vocabulary sharing. Our extensive experiments show that MLS consistently yields improvement over original label smoothing on different datasets, including bilingual and multilingual translation from both translation quality and model’s calibration. Our code is released at https://github.com/PKUnlp-icler/MLS
Contrastive Learning-Enhanced Nearest Neighbor Mechanism for Multi-Label Text Classification
Authors: Xi’ao Su, Ran Wang, Xinyu Dai
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Contrastive Learning, Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

Multi-Label Text Classification (MLTC) is a fundamental and challenging task in natural language processing. Previous studies mainly focus on learning text representation and modeling label correlation but neglect the rich knowledge from the existing similar instances when predicting labels of a specific text. To make up for this oversight, we propose a k nearest neighbor (kNN) mechanism which retrieves several neighbor instances and interpolates the model output with their labels. Moreover, we design a multi-label contrastive learning objective that makes the model aware of the kNN classification process and improves the quality of the retrieved neighbors while inference. Extensive experiments show that our method can bring consistent and significant performance improvement to multiple MLTC models including the state-of-the-art pretrained and non-pretrained ones.
Adjusting the Precision-Recall Trade-Off with Align-and-Predict Decoding for Grammatical Error Correction
Authors: Xin Sun, Houfeng Wang
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Grammatical Error Correction

Modern writing assistance applications are always equipped with a Grammatical Error Correction (GEC) model to correct errors in user-entered sentences. Different scenarios have varying requirements for correction behavior, e.g., performing more precise corrections (high precision) or providing more candidates for users (high recall). However, previous works adjust such trade-off only for sequence labeling approaches. In this paper, we propose a simple yet effective counterpart – Align-and-Predict Decoding (APD) for the most popular sequence-to-sequence models to offer more flexibility for the precision-recall trade-off. During inference, APD aligns the already generated sequence with input and adjusts scores of the following tokens. Experiments in both English and Chinese GEC benchmarks show that our approach not only adapts a single model to precision-oriented and recall-oriented inference, but also maximizes its potential to achieve state-of-the-art results. Our code is available at https://github.com/AutoTemp/Align-and-Predict.
On the Effect of Isotropy on VAE Representations of Text
Authors: Lan Zhang, Wray Buntine, Ehsan Shareghi
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5

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.
Rewarding Semantic Similarity under Optimized Alignments for AMR-to-Text Generation
Authors: Lisa Jin, Daniel Gildea
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: AMR-to-Text Generation, Reinforcement Learning (RL), Semantic Similarity, Semantic Textual Similarity, Text Generation

A common way to combat exposure bias is by applying scores from evaluation metrics as rewards in reinforcement learning (RL). Metrics leveraging contextualized embeddings appear more flexible than their n-gram matching counterparts and thus ideal as training rewards. However, metrics such as BERTScore greedily align candidate and reference tokens, which can allow system outputs to receive excess credit relative to a reference. Furthermore, past approaches featuring semantic similarity rewards suffer from repetitive outputs and overfitting. We address these issues by proposing metrics that replace the greedy alignments in BERTScore with optimized ones. We compute them on a model’s trained token embeddings to prevent domain mismatch. Our model optimizing discrete alignment metrics consistently outperforms cross-entropy and BLEU reward baselines on AMR-to-text generation. In addition, we find that this approach enjoys stable training compared to a non-RL setting.
Primum Non Nocere: Before working with Indigenous data, the ACL must confront ongoing colonialism
Authors: Lane Schwartz
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5

In this paper, we challenge the ACL community to reckon with historical and ongoing colonialism by adopting a set of ethical obligations and best practices drawn from the Indigenous studies literature. While the vast majority of NLP research focuses on a very small number of very high resource languages (English, Chinese, etc), some work has begun to engage with Indigenous languages. No research involving Indigenous language data can be considered ethical without first acknowledging that Indigenous languages are not merely very low resource languages. The toxic legacy of colonialism permeates every aspect of interaction between Indigenous communities and outside researchers. To this end, we propose that the ACL draft and adopt an ethical framework for NLP researchers and computational linguists wishing to engage in research involving Indigenous languages.
Unsupervised multiple-choice question generation for out-of-domain Q&A fine-tuning
Authors: Guillaume Le Berre, Christophe Cerisara, Philippe Langlais, Guy Lapalme
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Multiple-choice, Question Answering, Question Generation, Question-Generation

Pre-trained models have shown very good performances on a number of question answering benchmarks especially when fine-tuned on multiple question answering datasets at once. In this work, we propose an approach for generating a fine-tuning dataset thanks to a rule-based algorithm that generates questions and answers from unannotated sentences. We show that the state-of-the-art model UnifiedQA can greatly benefit from such a system on a multiple-choice benchmark about physics, biology and chemistry it has never been trained on. We further show that improved performances may be obtained by selecting the most challenging distractors (wrong answers), with a dedicated ranker based on a pretrained RoBERTa model.
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models’ Performance
Authors: Omer Goldman, David Guriel, Reut Tsarfaty
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: LEMMA, Morphological Inflection

In the domain of Morphology, Inflection is a fundamental and important task that gained a lot of traction in recent years, mostly via SIGMORPHON’s shared-tasks.With average accuracy above 0.9 over the scores of all languages, the task is considered mostly solved using relatively generic neural seq2seq models, even with little data provided.In this work, we propose to re-evaluate morphological inflection models by employing harder train-test splits that will challenge the generalization capacity of the models. In particular, as opposed to the naïve split-by-form, we propose a split-by-lemma method to challenge the performance on existing benchmarks.Our experiments with the three top-ranked systems on the SIGMORPHON’s 2020 shared-task show that the lemma-split presents an average drop of 30 percentage points in macro-average for the 90 languages included. The effect is most significant for low-resourced languages with a drop as high as 95 points, but even high-resourced languages lose about 10 points on average. Our results clearly show that generalizing inflection to unseen lemmas is far from being solved, presenting a simple yet effective means to promote more sophisticated models.
Evaluating zero-shot transfers and multilingual models for dependency parsing and POS tagging within the low-resource language family Tupían
Authors: Frederic Blum
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Dependency Parsing, POS, POS Tagging, Transfer Learning

This work presents two experiments with the goal of replicating the transferability of dependency parsers and POS taggers trained on closely related languages within the low-resource language family Tupían. The experiments include both zero-shot settings as well as multilingual models. Previous studies have found that even a comparably small treebank from a closely related language will improve sequence labelling considerably in such cases. Results from both POS tagging and dependency parsing confirm previous evidence that the closer the phylogenetic relation between two languages, the better the predictions for sequence labelling tasks get. In many cases, the results are improved if multiple languages from the same family are combined. This suggests that in addition to leveraging similarity between two related languages, the incorporation of multiple languages of the same family might lead to better results in transfer learning for NLP applications.
RFBFN: A Relation-First Blank Filling Network for Joint Relational Triple Extraction
Authors: Zhe Li, Luoyi Fu, Xinbing Wang, Haisong Zhang, Chenghu Zhou
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Relation

Joint relational triple extraction from unstructured text is an important task in information extraction. However, most existing works either ignore the semantic information of relations or predict subjects and objects sequentially. To address the issues, we introduce a new blank filling paradigm for the task, and propose a relation-first blank filling network (RFBFN). Specifically, we first detect potential relations maintained in the text to aid the following entity pair extraction. Then, we transform relations into relation templates with blanks which contain the fine-grained semantic representation of the relations. Finally, corresponding subjects and objects are extracted simultaneously by filling the blanks. We evaluate the proposed model on public benchmark datasets. Experimental results show our model outperforms current state-of-the-art methods. The source code of our work is available at: https://github.com/lizhe2016/RFBFN.
Darkness can not drive out darkness: Investigating Bias in Hate SpeechDetection Models
Authors: Fatma Elsafoury
Date: 2022-05-01 00:00:00
Proceeding: ACL 2022 5
Tasks: Abuse Detection, Hate Speech Detection

It has become crucial to develop tools for automated hate speech and abuse detection. These tools would help to stop the bullies and the haters and provide a safer environment for individuals especially from marginalized groups to freely express themselves. However, recent research shows that machine learning models are biased and they might make the right decisions for the wrong reasons. In this thesis, I set out to understand the performance of hate speech and abuse detection models and the different biases that could influence them. I show that hate speech and abuse detection models are not only subject to social bias but also to other types of bias that have not been explored before. Finally, I investigate the causal effect of the social and intersectional bias on the performance and unfairness of hate speech detection models.