mtybilly/Instruct2DS huggingface dataset:
Plug-and-Play Adaptation for Continuously-updated QA
Authors: Kyungjae Lee, Wookje Han, Seung-won Hwang, Hwaran Lee, Joonsuk Park, Sang-Woo Lee
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Language models (LMs) have shown great potential as implicit knowledge bases (KBs). And for their practical use, knowledge in LMs need to be updated periodically. However, existing tasks to assess LMs' efficacy as KBs do not adequately consider multiple large-scale updates. To this end, we first propose a novel task---Continuously-updated QA (CuQA)---in which multiple large-scale updates are made to LMs, and the performance is measured with respect to the success in adding and updating knowledge while retaining existing knowledge. We then present LMs with plug-in modules that effectively handle the updates. Experiments conducted on zsRE QA and NQ datasets show that our method outperforms existing approaches. We find that our method is 4x more effective in terms of updates/forgets ratio, compared to a fine-tuning baseline.
Reinforced Cross-modal Alignment for Radiology Report Generation
Authors: Han Qin, Yan Song
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Medical images are widely used in clinical decision-making, where writing radiology reports is a potential application that can be enhanced by automatic solutions to alleviate physicians' workload. In general, radiology report generation is an image-text task, where cross-modal mappings between images and texts play an important role in generating high-quality reports. Although previous studies attempt to facilitate the alignment via the co-attention mechanism under supervised settings, they suffer from lacking valid and accurate correspondences due to no annotation of such alignment. In this paper, we propose an approach with reinforcement learning (RL) over a cross-modal memory (CMM) to better align visual and textual features for radiology report generation. In detail, a shared memory is used to record the mappings between visual and textual information, and the proposed reinforced algorithm is performed to learn the signal from the reports to guide the cross-modal alignment even though such reports are not directly related to how images and texts are mapped. Experimental results on two English radiology report datasets, i.e., IU X-Ray and MIMIC-CXR, show the effectiveness of our approach, where the state-of-the-art results are achieved. We further conduct human evaluation and case study which confirm the validity of the reinforced algorithm in our approach.
What Works and Doesn`t Work, A Deep Decoder for Neural Machine Translation
Authors: Zuchao Li, Yiran Wang, Masao Utiyama, Eiichiro Sumita, Hai Zhao, Taro Watanabe
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Deep learning has demonstrated performance advantages in a wide range of natural language processing tasks, including neural machine translation (NMT). Transformer NMT models are typically strengthened by deeper encoder layers, but deepening their decoder layers usually results in failure. In this paper, we first identify the cause of the failure of the deep decoder in the Transformer model. Inspired by this discovery, we then propose approaches to improving it, with respect to model structure and model training, to make the deep decoder practical in NMT. Specifically, with respect to model structure, we propose a cross-attention drop mechanism to allow the decoder layers to perform their own different roles, to reduce the difficulty of deep-decoder learning. For model training, we propose a collapse reducing training approach to improve the stability and effectiveness of deep-decoder training. We experimentally evaluated our proposed Transformer NMT model structure modification and novel training methods on several popular machine translation benchmarks. The results showed that deepening the NMT model by increasing the number of decoder layers successfully prevented the deepened decoder from degrading to an unconditional language model. In contrast to prior work on deepening an NMT model on the encoder, our method can deepen the model on both the encoder and decoder at the same time, resulting in a deeper model and improved performance.
SyMCoM - Syntactic Measure of Code Mixing A Study Of English-Hindi Code-Mixing
Authors: Prashant Kodali, Anmol Goel, Monojit Choudhury, Manish Shrivastava, Ponnurangam Kumaraguru
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Code mixing is the linguistic phenomenon where bilingual speakers tend to switch between two or more languages in conversations. Recent work on code-mixing in computational settings has leveraged social media code mixed texts to train NLP models. For capturing the variety of code mixing in, and across corpus, Language ID (LID) tags based measures (CMI) have been proposed. Syntactical variety/patterns of code-mixing and their relationship vis-a-vis computational model`s performance is under explored. In this work, we investigate a collection of English(en)-Hindi(hi) code-mixed datasets from a syntactic lens to propose, $SyMCoM$, an indicator of syntactic variety in code-mixed text, with intuitive theoretical bounds. We train SoTA en-hi PoS tagger, accuracy of 93.4%, to reliably compute PoS tags on a corpus, and demonstrate the utility of $SyMCoM$ by applying it on various syntactical categories on a collection of datasets, and compare datasets using the measure.
HybriDialogue: An Information-Seeking Dialogue Dataset Grounded on Tabular and Textual Data
Authors: Kai Nakamura, Sharon Levy, Yi-Lin Tuan, Wenhu Chen, William Yang Wang
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: A pressing challenge in current dialogue systems is to successfully converse with users on topics with information distributed across different modalities. Previous work in multiturn dialogue systems has primarily focused on either text or table information. In more realistic scenarios, having a joint understanding of both is critical as knowledge is typically distributed over both unstructured and structured forms. We present a new dialogue dataset, HybriDialogue, which consists of crowdsourced natural conversations grounded on both Wikipedia text and tables. The conversations are created through the decomposition of complex multihop questions into simple, realistic multiturn dialogue interactions. We propose retrieval, system state tracking, and dialogue response generation tasks for our dataset and conduct baseline experiments for each. Our results show that there is still ample opportunity for improvement, demonstrating the importance of building stronger dialogue systems that can reason over the complex setting of informationseeking dialogue grounded on tables and text.
NEWTS: A Corpus for News Topic-Focused Summarization
Authors: Seyed Ali Bahrainian, Sheridan Feucht, Carsten Eickhoff
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Text summarization models are approaching human levels of fidelity. Existing benchmarking corpora provide concordant pairs of full and abridged versions of Web, news or professional content. To date, all summarization datasets operate under a one-size-fits-all paradigm that may not reflect the full range of organic summarization needs. Several recently proposed models (e.g., plug and play language models) have the capacity to condition the generated summaries on a desired range of themes. These capacities remain largely unused and unevaluated as there is no dedicated dataset that would support the task of topic-focused summarization. This paper introduces the first topical summarization corpus NEWTS, based on the well-known CNN/Dailymail dataset, and annotated via online crowd-sourcing. Each source article is paired with two reference summaries, each focusing on a different theme of the source document. We evaluate a representative range of existing techniques and analyze the effectiveness of different prompting methods.
Classification without (Proper) Representation: Political Heterogeneity in Social Media and Its Implications for Classification and Behavioral Analysis
Authors: Kenan Alkiek, Bohan Zhang, David Jurgens
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Reddit is home to a broad spectrum of political activity, and users signal their political affiliations in multiple ways---from self-declarations to community participation. Frequently, computational studies have treated political users as a single bloc, both in developing models to infer political leaning and in studying political behavior. Here, we test this assumption of political users and show that commonly-used political-inference models do not generalize, indicating heterogeneous types of political users. The models remain imprecise at best for most users, regardless of which sources of data or methods are used. Across a 14-year longitudinal analysis, we demonstrate that the choice in definition of a political user has significant implications for behavioral analysis. Controlling for multiple factors, political users are more toxic on the platform and inter-party interactions are even more toxic---but not all political users behave this way. Last, we identify a subset of political users who repeatedly flip affiliations, showing that these users are the most controversial of all, acting as provocateurs by more frequently bringing up politics, and are more likely to be banned, suspended, or deleted.
Toward More Meaningful Resources for Lower-resourced Languages
Authors: Constantine Lignos, Nolan Holley, Chester Palen-Michel, Jonne Sälevä
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: In this position paper, we describe our perspective on how meaningful resources for lower-resourced languages should be developed in connection with the speakers of those languages. Before advancing that position, we first examine two massively multilingual resources used in language technology development, identifying shortcomings that limit their usefulness. We explore the contents of the names stored in Wikidata for a few lower-resourced languages and find that many of them are not in fact in the languages they claim to be, requiring non-trivial effort to correct. We discuss quality issues present in WikiAnn and evaluate whether it is a useful supplement to hand-annotated data. We then discuss the importance of creating annotations for lower-resourced languages in a thoughtful and ethical way that includes the language speakers as part of the development process. We conclude with recommended guidelines for resource development.
Better Quality Estimation for Low Resource Corpus Mining
Authors: Muhammed Kocyigit, Jiho Lee, Derry Wijaya
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Quality Estimation (QE) models have the potential to change how we evaluate and maybe even train machine translation models. However, these models still lack the robustness to achieve general adoption. We show that Stateof-the-art QE models, when tested in a Parallel Corpus Mining (PCM) setting, perform unexpectedly bad due to a lack of robustness to out-of-domain examples. We propose a combination of multitask training, data augmentation and contrastive learning to achieve better and more robust QE performance. We show that our method improves QE performance significantly in the MLQE challenge and the robustness of QE models when tested in the Parallel Corpus Mining setup. We increase the accuracy in PCM by more than 0.80, making it on par with state-of-the-art PCM methods that use millions of sentence pairs to train their models. In comparison, we use a thousand times less data, 7K parallel sentences in total, and propose a novel low resource PCM method.
End-to-End Segmentation-based News Summarization
Authors: Yang Liu, Chenguang Zhu, Michael Zeng
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: In this paper, we bring a new way of digesting news content by introducing the task of segmenting a news article into multiple sections and generating the corresponding summary to each section. We make two contributions towards this new task. First, we create and make available a dataset, SegNews, consisting of 27k news articles with sections and aligned heading-style section summaries. Second, we propose a novel segmentation-based language generation model adapted from pre-trained language models that can jointly segment a document and produce the summary for each section. Experimental results on SegNews demonstrate that our model can outperform several state-of-the-art sequence-to-sequence generation models for this new task.
Fast Nearest Neighbor Machine Translation
Authors: Yuxian Meng, Xiaoya Li, Xiayu Zheng, Fei Wu, Xiaofei Sun, Tianwei Zhang, Jiwei Li
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Though nearest neighbor Machine Translation ($k$NN-MT) (CITATION) has proved to introduce significant performance boosts over standard neural MT systems, it is prohibitively slow since it uses the entire reference corpus as the datastore for the nearest neighbor search. This means each step for each beam in the beam search has to search over the entire reference corpus. $k$NN-MT is thus two-orders slower than vanilla MT models, making it hard to be applied to real-world applications, especially online services. In this work, we propose Fast $k$NN-MT to address this issue. Fast $k$NN-MT constructs a significantly smaller datastore for the nearest neighbor search: for each word in a source sentence, Fast $k$NN-MT first selects its nearest token-level neighbors, which is limited to tokens that are the same as the query token. Then at each decoding step, in contrast to using the entire corpus as the datastore, the search space is limited to target tokens corresponding to the previously selected reference source tokens. This strategy avoids search through the whole datastore for nearest neighbors and drastically improves decoding efficiency. Without loss of performance, Fast $k$NN-MT is two-orders faster than $k$NN-MT, and is only two times slower than the standard NMT model. Fast $k$NN-MT enables the practical use of $k$NN-MT systems in real-world MT applications. The code is available at r̆lhttps://github.com/ShannonAI/fast-knn-nmt.
Extracting Latent Steering Vectors from Pretrained Language Models
Authors: Nishant Subramani, Nivedita Suresh, Matthew Peters
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Prior work on controllable text generation has focused on learning how to control language models through trainable decoding, smart-prompt design, or fine-tuning based on a desired objective. We hypothesize that the information needed to steer the model to generate a target sentence is already encoded within the model. Accordingly, we explore a different approach altogether: extracting latent vectors directly from pretrained language model decoders without fine-tuning. Experiments show that there exist steering vectors, which, when added to the hidden states of the language model, generate a target sentence nearly perfectly (\ensuremath> 99 BLEU) for English sentences from a variety of domains. We show that vector arithmetic can be used for unsupervised sentiment transfer on the Yelp sentiment benchmark, with performance comparable to models tailored to this task. We find that distances between steering vectors reflect sentence similarity when evaluated on a textual similarity benchmark (STS-B), outperforming pooled hidden states of models. Finally, we present an analysis of the intrinsic properties of the steering vectors. Taken together, our results suggest that frozen LMs can be effectively controlled through their latent steering space.
Domain Generalisation of NMT: Fusing Adapters with Leave-One-Domain-Out Training
Authors: Thuy-Trang Vu, Shahram Khadivi, Dinh Phung, Gholamreza Haffari
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Generalising to unseen domains is under-explored and remains a challenge in neural machine translation. Inspired by recent research in parameter-efficient transfer learning from pretrained models, this paper proposes a fusion-based generalisation method that learns to combine domain-specific parameters. We propose a leave-one-domain-out training strategy to avoid information leaking to address the challenge of not knowing the test domain during training time. Empirical results on three language pairs show that our proposed fusion method outperforms other baselines up to +0.8 BLEU score on average.
Reframing Instructional Prompts to GPTk`s Language
Authors: Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, Hannaneh Hajishirzi
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: What kinds of instructional prompts are easier to follow for Language Models (LMs)? We study this question by conducting extensive empirical analysis that shed light on important features of successful instructional prompts. Specifically, we study several classes of reframing techniques for manual reformulation of prompts into more effective ones. Some examples include decomposing a complex task instruction into multiple simpler tasks or itemizing instructions into sequential steps. Our experiments compare the zero-shot and few-shot performance of LMs prompted with reframed instructions on 12 NLP tasks across 6 categories. Compared with original instructions, our reframed instructions lead to significant improvements across LMs with different sizes. For example, the same reframed prompts boost few-shot performance of GPT3-series and GPT2-series by 12.5% and 6.7% respectively averaged over all tasks. Furthermore, reframed instructions reduce the number of examples required to prompt LMs in the few-shot setting. We hope these empirically-driven techniques will pave the way towards more effective future prompting algorithms.
Read Top News First: A Document Reordering Approach for Multi-Document News Summarization
Authors: Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury, Muthu Kumar Chandrasekaran, Kathleen McKeown, Snigdha Chaturvedi
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document. However, this method neglects the relative importance of documents. We propose a simple approach to reorder the documents according to their relative importance before concatenating and summarizing them. The reordering makes the salient content easier to learn by the summarization model. Experiments show that our approach outperforms previous state-of-the-art methods with more complex architectures.
Human Language Modeling
Authors: Nikita Soni, Matthew Matero, Niranjan Balasubramanian, H. Andrew Schwartz
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Natural language is generated by people, yet traditional language modeling views words or documents as if generated independently. Here, we propose human language modeling (HuLM), a hierarchical extension to the language modeling problem where by a human- level exists to connect sequences of documents (e.g. social media messages) and capture the notion that human language is moderated by changing human states. We introduce, HaRT, a large-scale transformer model for solving HuLM, pre-trained on approximately 100,000 social media users, and demonstrate it`s effectiveness in terms of both language modeling (perplexity) for social media and fine-tuning for 4 downstream tasks spanning document- and user-levels. Results on all tasks meet or surpass the current state-of-the-art.
Inverse is Better! Fast and Accurate Prompt for Few-shot Slot Tagging
Authors: Yutai Hou, Cheng Chen, Xianzhen Luo, Bohan Li, Wanxiang Che
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Prompting methods recently achieve impressive success in few-shot learning. These methods modify input samples with prompt sentence pieces, and decode label tokens to map samples to corresponding labels. However, such a paradigm is very inefficient for the task of slot tagging. Since slot tagging samples are multiple consecutive words in a sentence, the prompting methods have to enumerate all n-grams token spans to find all the possible slots, which greatly slows down the prediction. To tackle this, we introduce an inverse paradigm for prompting. Different from the classic prompts mapping tokens to labels, we reversely predict slot values given slot types. Such inverse prompting only requires a one-turn prediction for each slot type and greatly speeds up the prediction. Besides, we propose a novel Iterative Prediction Strategy, from which the model learns to refine predictions by considering the relations between different slot types. We find, somewhat surprisingly, the proposed method not only predicts faster but also significantly improves the effect (improve over 6.1 F1-scores on 10-shot setting) and achieves new state-of-the-art performance.
Cross-Modal Cloze Task: A New Task to Brain-to-Word Decoding
Authors: Shuxian Zou, Shaonan Wang, Jiajun Zhang, Chengqing Zong
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Decoding language from non-invasive brain activity has attracted increasing attention from both researchers in neuroscience and natural language processing. Due to the noisy nature of brain recordings, existing work has simplified brain-to-word decoding as a binary classification task which is to discriminate a brain signal between its corresponding word and a wrong one. This pairwise classification task, however, cannot promote the development of practical neural decoders for two reasons. First, it has to enumerate all pairwise combinations in the test set, so it is inefficient to predict a word in a large vocabulary. Second, a perfect pairwise decoder cannot guarantee the performance on direct classification. To overcome these and go a step further to a realistic neural decoder, we propose a novel Cross-Modal Cloze (CMC) task which is to predict the target word encoded in the neural image with a context as prompt. Furthermore, to address this task, we propose a general approach that leverages the pre-trained language model to predict the target word. To validate our method, we perform experiments on more than 20 participants from two brain imaging datasets. Our method achieves 28.91% top-1 accuracy and 54.19% top-5 accuracy on average across all participants, significantly outperforming several baselines. This result indicates that our model can serve as a state-of-the-art baseline for the CMC task. More importantly, it demonstrates that it is feasible to decode a certain word within a large vocabulary from its neural brain activity.
Mitigating Gender Bias in Distilled Language Models via Counterfactual Role Reversal
Authors: Umang Gupta, Jwala Dhamala, Varun Kumar, Apurv Verma, Yada Pruksachatkun, Satyapriya Krishna, Rahul Gupta, Kai-Wei Chang, Greg Ver Steeg, Aram Galstyan
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Language models excel at generating coherent text, and model compression techniques such as knowledge distillation have enabled their use in resource-constrained settings. However, these models can be biased in multiple ways, including the unfounded association of male and female genders with gender-neutral professions. Therefore, knowledge distillation without any fairness constraints may preserve or exaggerate the teacher model`s biases onto the distilled model. To this end, we present a novel approach to mitigate gender disparity in text generation by learning a fair model during knowledge distillation. We propose two modifications to the base knowledge distillation based on counterfactual role reversal---modifying teacher probabilities and augmenting the training set. We evaluate gender polarity across professions in open-ended text generated from the resulting distilled and finetuned GPT--2 models and demonstrate a substantial reduction in gender disparity with only a minor compromise in utility. Finally, we observe that language models that reduce gender polarity in language generation do not improve embedding fairness or downstream classification fairness.
Domain Representative Keywords Selection: A Probabilistic Approach
Authors: Pritom Saha Akash, Jie Huang, Kevin Chang, Yunyao Li, Lucian Popa, ChengXiang Zhai
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: We propose a probabilistic approach to select a subset of a \textittarget domain representative keywords from a candidate set, contrasting with a context domain. Such a task is crucial for many downstream tasks in natural language processing. To contrast the target domain and the context domain, we adapt the \textittwo-component mixture model concept to generate a distribution of candidate keywords. It provides more importance to the \textitdistinctive keywords of the target domain than common keywords contrasting with the context domain. To support the \textitrepresentativeness of the selected keywords towards the target domain, we introduce an \textitoptimization algorithm for selecting the subset from the generated candidate distribution. We have shown that the optimization algorithm can be efficiently implemented with a near-optimal approximation guarantee. Finally, extensive experiments on multiple domains demonstrate the superiority of our approach over other baselines for the tasks of keyword summary generation and trending keywords selection.
Hierarchical Inductive Transfer for Continual Dialogue Learning
Authors: Shaoxiong Feng, Xuancheng Ren, Kan Li, Xu Sun
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Pre-trained models have achieved excellent performance on the dialogue task. However, for the continual increase of online chit-chat scenarios, directly fine-tuning these models for each of the new tasks not only explodes the capacity of the dialogue system on the embedded devices but also causes knowledge forgetting on pre-trained models and knowledge interference among diverse dialogue tasks. In this work, we propose a hierarchical inductive transfer framework to learn and deploy the dialogue skills continually and efficiently. First, we introduce the adapter module into pre-trained models for learning new dialogue tasks. As the only trainable module, it is beneficial for the dialogue system on the embedded devices to acquire new dialogue skills with negligible additional parameters. Then, for alleviating knowledge interference between tasks yet benefiting the regularization between them, we further design hierarchical inductive transfer that enables new tasks to use general knowledge in the base adapter without being misled by diverse knowledge in task-specific adapters. Empirical evaluation and analysis indicate that our framework obtains comparable performance under deployment-friendly model capacity.
Why Exposure Bias Matters: An Imitation Learning Perspective of Error Accumulation in Language Generation
Authors: Kushal Arora, Layla El Asri, Hareesh Bahuleyan, Jackie Cheung
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Current language generation models suffer from issues such as repetition, incoherence, and hallucinations. An often-repeated hypothesis for this brittleness of generation models is that it is caused by the training and the generation procedure mismatch, also referred to as exposure bias. In this paper, we verify this hypothesis by analyzing exposure bias from an imitation learning perspective. We show that exposure bias leads to an accumulation of errors during generation, analyze why perplexity fails to capture this accumulation of errors, and empirically show that this accumulation results in poor generation quality.
Question Answering Infused Pre-training of General-Purpose Contextualized Representations
Authors: Robin Jia, Mike Lewis, Luke Zettlemoyer
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: We propose a pre-training objective based on question answering (QA) for learning general-purpose contextual representations, motivated by the intuition that the representation of a phrase in a passage should encode all questions that the phrase can answer in context. To this end, we train a bi-encoder QA model, which independently encodes passages and questions, to match the predictions of a more accurate cross-encoder model on 80 million synthesized QA pairs. By encoding QA-relevant information, the bi-encoder`s token-level representations are useful for non-QA downstream tasks without extensive (or in some cases, any) fine-tuning. We show large improvements over both RoBERTa-large and previous state-of-the-art results on zero-shot and few-shot paraphrase detection on four datasets, few-shot named entity recognition on two datasets, and zero-shot sentiment analysis on three datasets.
Automatic Song Translation for Tonal Languages
Authors: Fenfei Guo, Chen Zhang, Zhirui Zhang, Qixin He, Kejun Zhang, Jun Xie, Jordan Boyd-Graber
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: This paper develops automatic song translation (AST) for tonal languages and addresses the unique challenge of aligning words' tones with melody of a song in addition to conveying the original meaning. We propose three criteria for effective AST---preserving meaning, singability and intelligibility---and design metrics for these criteria. We develop a new benchmark for English--Mandarin song translation and develop an unsupervised AST system, Guided AliGnment for Automatic Song Translation (GagaST), which combines pre-training with three decoding constraints. Both automatic and human evaluations show GagaST successfully balances semantics and singability.
Read before Generate! Faithful Long Form Question Answering with Machine Reading
Authors: Dan Su, Xiaoguang Li, Jindi Zhang, Lifeng Shang, Xin Jiang, Qun Liu, Pascale Fung
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Long-form question answering (LFQA) aims to generate a paragraph-length answer for a given question. While current work on LFQA using large pre-trained model for generation are effective at producing fluent and somewhat relevant content, one primary challenge lies in how to generate a faithful answer that has less hallucinated content. We propose a new end-to-end framework that jointly models answer generation and machine reading. The key idea is to augment the generation model with fine-grained, answer-related salient information which can be viewed as an emphasis on faithful facts. State-of-the-art results on two LFQA datasets, ELI5 and MS MARCO, demonstrate the effectiveness of our method, in comparison with strong baselines on automatic and human evaluation metrics. A detailed analysis further proves the competency of our methods in generating fluent, relevant, and more faithful answers.
A Simple yet Effective Relation Information Guided Approach for Few-Shot Relation Extraction
Authors: Yang Liu, Jinpeng Hu, Xiang Wan, Tsung-Hui Chang
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Few-Shot Relation Extraction aims at predicting the relation for a pair of entities in a sentence by training with a few labelled examples in each relation. Some recent works have introduced relation information (i.e., relation labels or descriptions) to assist model learning based on Prototype Network. However, most of them constrain the prototypes of each relation class implicitly with relation information, generally through designing complex network structures, like generating hybrid features, combining with contrastive learning or attention networks. We argue that relation information can be introduced more explicitly and effectively into the model. Thus, this paper proposes a direct addition approach to introduce relation information. Specifically, for each relation class, the relation representation is first generated by concatenating two views of relations (i.e., [CLS] token embedding and the mean value of embeddings of all tokens) and then directly added to the original prototype for both train and prediction. Experimental results on the benchmark dataset FewRel 1.0 show significant improvements and achieve comparable results to the state-of-the-art, which demonstrates the effectiveness of our proposed approach. Besides, further analyses verify that the direct addition is a much more effective way to integrate the relation representations and the original prototypes.
MIMICause: Representation and automatic extraction of causal relation types from clinical notes
Authors: Vivek Khetan, Md Imbesat Rizvi, Jessica Huber, Paige Bartusiak, Bogdan Sacaleanu, Andrew Fano
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Understanding causal narratives communicated in clinical notes can help make strides towards personalized healthcare. Extracted causal information from clinical notes can be combined with structured EHR data such as patients' demographics, diagnoses, and medications. This will enhance healthcare providers' ability to identify aspects of a patient`s story communicated in the clinical notes and help make more informed decisions. In this work, we propose annotation guidelines, develop an annotated corpus and provide baseline scores to identify types and direction of causal relations between a pair of biomedical concepts in clinical notes; communicated implicitly or explicitly, identified either in a single sentence or across multiple sentences. We annotate a total of 2714 de-identified examples sampled from the 2018 n2c2 shared task dataset and train four different language model based architectures. Annotation based on our guidelines achieved a high inter-annotator agreement i.e. Fleiss' kappa ($ąppa$) score of 0.72, and our model for identification of causal relations achieved a macro F1 score of 0.56 on the test data. The high inter-annotator agreement for clinical text shows the quality of our annotation guidelines while the provided baseline F1 score sets the direction for future research towards understanding narratives in clinical texts.
Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation
Authors: Xuandong Zhao, Zhiguo Yu, Ming Wu, Lei Li
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: How to learn highly compact yet effective sentence representation? Pre-trained language models have been effective in many NLP tasks. However, these models are often huge and produce large sentence embeddings. Moreover, there is a big performance gap between large and small models. In this paper, we propose Homomorphic Projective Distillation (HPD) to learn compressed sentence embeddings. Our method augments a small Transformer encoder model with learnable projection layers to produce compact representations while mimicking a large pre-trained language model to retain the sentence representation quality. We evaluate our method with different model sizes on both semantic textual similarity (STS) and semantic retrieval (SR) tasks. Experiments show that our method achieves 2.7-4.5 points performance gain on STS tasks compared with previous best representations of the same size. In SR tasks, our method improves retrieval speed (8.2×) and memory usage (8.0×) compared with state-of-the-art large models. Our implementation is available at r̆lhttps://github.com/XuandongZhao/HPD.
Debiasing Event Understanding for Visual Commonsense Tasks
Authors: Minji Seo, YeonJoon Jung, Seungtaek Choi, Seung-won Hwang, Bei Liu
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: We study event understanding as a critical step towards visual commonsense tasks. Meanwhile, we argue that current object-based event understanding is purely likelihood-based, leading to incorrect event prediction, due to biased correlation between events and objects. We propose to mitigate such biases with $do$-calculus, proposed in causality research, but overcoming its limited robustness, by an optimized aggregation with association-based prediction.We show the effectiveness of our approach, intrinsically by comparing our generated events with ground-truth event annotation, and extrinsically by downstream commonsense tasks.
Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs
Authors: Yao Zhang, Peiyao Li, Hongru Liang, Adam Jatowt, Zhenglu Yang
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Current Question Answering over Knowledge Graphs (KGQA) task mainly focuses on performing answer reasoning upon KGs with binary facts. However, it neglects the n-ary facts, which contain more than two entities. In this work, we highlight a more challenging but under-explored task: n-ary KGQA, i.e., answering n-ary facts questions upon n-ary KGs. Nevertheless, the multi-hop reasoning framework popular in binary KGQA task is not directly applicable on n-ary KGQA. We propose two feasible improvements: 1) upgrade the basic reasoning unit from entity or relation to fact, and 2) upgrade the reasoning structure from chain to tree. Therefore, we propose a novel fact-tree reasoning framework, FacTree, which integrates the above two upgrades. FacTree transforms the question into a fact tree and performs iterative fact reasoning on the fact tree to infer the correct answer. Experimental results on the n-ary KGQA dataset we constructed and two binary KGQA benchmarks demonstrate the effectiveness of FacTree compared with state-of-the-art methods.
DeepStruct: Pretraining of Language Models for Structure Prediction
Authors: Chenguang Wang, Xiao Liu, Zui Chen, Haoyun Hong, Jie Tang, Dawn Song
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: We introduce a method for improving the structural understanding abilities of language models. Unlike previous approaches that finetune the models with task-specific augmentation, we pretrain language models to generate structures from the text on a collection of task-agnostic corpora. Our structure pretraining enables zero-shot transfer of the learned knowledge that models have about the structure tasks. We study the performance of this approach on 28 datasets, spanning 10 structure prediction tasks including open information extraction, joint entity and relation extraction, named entity recognition, relation classification, semantic role labeling, event extraction, coreference resolution, factual probe, intent detection, and dialogue state tracking. We further enhance the pretraining with the task-specific training sets. We show that a 10B parameter language model transfers non-trivially to most tasks and obtains state-of-the-art performance on 21 of 28 datasets that we evaluate. Our code and datasets will be made publicly available.
The Change that Matters in Discourse Parsing: Estimating the Impact of Domain Shift on Parser Error
Authors: Katherine Atwell, Anthony Sicilia, Seong Jae Hwang, Malihe Alikhani
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Discourse analysis allows us to attain inferences of a text document that extend beyond the sentence-level. The current performance of discourse models is very low on texts outside of the training distribution`s coverage, diminishing the practical utility of existing models. There is need for a measure that can inform us to what extent our model generalizes from the training to the test sample when these samples may be drawn from distinct distributions. While this can be estimated via distribution shift, we argue that this does not directly correlate with change in the observed error of a classifier (i.e. error-gap). Thus, we propose to use a statistic from the theoretical domain adaptation literature which can be directly tied to error-gap. We study the bias of this statistic as an estimator of error-gap both theoretically and through a large-scale empirical study of over 2400 experiments on 6 discourse datasets from domains including, but not limited to: news, biomedical texts, TED talks, Reddit posts, and fiction. Our results not only motivate our proposal and help us to understand its limitations, but also provide insight on the properties of discourse models and datasets which improve performance in domain adaptation. For instance, we find that non-news datasets are slightly easier to transfer to than news datasets when the training and test sets are very different. Our code and an associated Python package are available to allow practitioners to make more informed model and dataset choices.
Mukayese: Turkish NLP Strikes Back
Authors: Ali Safaya, Emirhan Kurtuluş, Arda Goktogan, Deniz Yuret
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Having sufficient resources for language X lifts it from the under-resourced languages class, but not necessarily from the under-researched class. In this paper, we address the problem of the absence of organized benchmarks in the Turkish language. We demonstrate that languages such as Turkish are left behind the state-of-the-art in NLP applications. As a solution, we present Mukayese, a set of NLP benchmarks for the Turkish language that contains several NLP tasks. We work on one or more datasets for each benchmark and present two or more baselines. Moreover, we present four new benchmarking datasets in Turkish for language modeling, sentence segmentation, and spell checking. All datasets and baselines are available under: r̆lhttps://github.com/alisafaya/mukayese
Virtual Augmentation Supported Contrastive Learning of Sentence Representations
Authors: Dejiao Zhang, Wei Xiao, Henghui Zhu, Xiaofei Ma, Andrew Arnold
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Despite profound successes, contrastive representation learning relies on carefully designed data augmentations using domain-specific knowledge. This challenge is magnified in natural language processing, where no general rules exist for data augmentation due to the discrete nature of natural language. We tackle this challenge by presenting a Virtual augmentation Supported Contrastive Learning of sentence representations (VaSCL). Originating from the interpretation that data augmentation essentially constructs the neighborhoods of each training instance, we, in turn, utilize the neighborhood to generate effective data augmentations. Leveraging the large training batch size of contrastive learning, we approximate the neighborhood of an instance via its K-nearest in-batch neighbors in the representation space. We then define an instance discrimination task regarding the neighborhood and generate the virtual augmentation in an adversarial training manner. We access the performance of VaSCL on a wide range of downstream tasks and set a new state-of-the-art for unsupervised sentence representation learning.
MoEfication: Transformer Feed-forward Layers are Mixtures of Experts
Authors: Zhengyan Zhang, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, Jie Zhou
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Recent work has shown that feed-forward networks (FFNs) in pre-trained Transformers are a key component, storing various linguistic and factual knowledge. However, the computational patterns of FFNs are still unclear. In this work, we study the computational patterns of FFNs and observe that most inputs only activate a tiny ratio of neurons of FFNs. This phenomenon is similar to the sparsity of the human brain, which drives research on functional partitions of the human brain. To verify whether functional partitions also emerge in FFNs, we propose to convert a model into its MoE version with the same parameters, namely MoEfication. Specifically, MoEfication consists of two phases: (1) splitting the parameters of FFNs into multiple functional partitions as experts, and (2) building expert routers to decide which experts will be used for each input. Experimental results show that MoEfication can conditionally use 10% to 30% of FFN parameters while maintaining over 95% original performance for different models on various downstream tasks. Besides, MoEfication brings two advantages: (1) it significantly reduces the FLOPS of inference, i.e., 2x speedup with 25% of FFN parameters, and (2) it provides a fine-grained perspective to study the inner mechanism of FFNs. The source code of this paper can be obtained from r̆lhttps://github.com/thunlp/MoEfication.
DS-TOD: Efficient Domain Specialization for Task-Oriented Dialog
Authors: Chia-Chien Hung, Anne Lauscher, Simone Ponzetto, Goran Glavaš
Published in: Findings of the Association for Computational Linguistics: ACL 2022 (Findings Track), 2022

Abstract: Recent work has shown that self-supervised dialog-specific pretraining on large conversational datasets yields substantial gains over traditional language modeling (LM) pretraining in downstream task-oriented dialog (TOD). These approaches, however, exploit general dialogic corpora (e.g., Reddit) and thus presumably fail to reliably embed domain-specific knowledge useful for concrete downstream TOD domains. In this work, we investigate the effects of domain specialization of pretrained language models (PLMs) for TOD. Within our DS-TOD framework, we first automatically extract salient domain-specific terms, and then use them to construct DomainCC and DomainReddit -- resources that we leverage for domain-specific pretraining, based on (i) masked language modeling (MLM) and (ii) response selection (RS) objectives, respectively. We further propose a resource-efficient and modular domain specialization by means of domain adapters -- additional parameter-light layers in which we encode the domain knowledge. Our experiments with prominent TOD tasks -- dialog state tracking (DST) and response retrieval (RR) -- encompassing five domains from the MultiWOZ benchmark demonstrate the effectiveness of DS-TOD. Moreover, we show that the light-weight adapter-based specialization (1) performs comparably to full fine-tuning in single domain setups and (2) is particularly suitable for multi-domain specialization, where besides advantageous computational footprint, it can offer better TOD performance.