GenAI4ELab/papercli-papers huggingface dataset:
Natural Language to Code Generation in Interactive Data Science Notebooks
Authors: Pengcheng Yin; Wen-Ding Li; Kefan Xiao; Abhishek Rao; Yeming Wen; Kensen Shi; Joshua Howland; Paige Bailey; Michele Catasta; Henryk Michalewski; Oleksandr Polozov; Charles Sutton
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Computational notebooks, such as Jupyter notebooks, are interactive computing environments that are ubiquitous among data scientists to perform data wrangling and analytic tasks. To measure the performance of AI pair programmers that automatically synthesize programs for those tasks given natural language (NL) intents from users, we build ARCADE, a benchmark of 1078 code generation problems using the pandas data analysis framework in data science notebooks. ARCADE features multiple rounds of NL-to-code problems from the same notebook. It requires a model to understand rich multi-modal contexts, such as existing notebook cells and their execution states as well as previous turns of interaction. To establish a strong baseline on this challenging task, we develop PaChiNCo, a 62B code language model (LM) for Python computational notebooks, which significantly outperforms public code LMs. Finally, we explore few-shot prompting strategies to elicit better code with step-by-step decomposition and NL explanation, showing the potential to improve the diversity and explainability of model predictions. Arcade is publicly available at https://github.com/google-research/arcade-nl2code/.
Subset Retrieval Nearest Neighbor Machine Translation
Authors: Hiroyuki Deguchi; Taro Watanabe; Yusuke Matsui; Masao Utiyama; Hideki Tanaka; Eiichiro Sumita
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: k-nearest-neighbor machine translation (kNN-MT) (Khandelwal et al., 2021) boosts the translation performance of trained neural machine translation (NMT) models by incorporating example-search into the decoding algorithm. However, decoding is seriously time-consuming, i.e., roughly 100 to 1,000 times slower than standard NMT, because neighbor tokens are retrieved from all target tokens of parallel data in each timestep. In this paper, we propose “Subset kNN-MT”, which improves the decoding speed of kNN-MT by two methods: (1) retrieving neighbor target tokens from a subset that is the set of neighbor sentences of the input sentence, not from all sentences, and (2) efficient distance computation technique that is suitable for subset neighbor search using a look-up table. Our proposed method achieved a speed-up of up to 132.2 times and an improvement in BLEU score of up to 1.6 compared with kNN-MT in the WMT’19 De-En translation task and the domain adaptation tasks in De-En and En-Ja.
MIL-Decoding: Detoxifying Language Models at Token-Level via Multiple Instance Learning
Authors: Xu Zhang; Xiaojun Wan
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Despite advances in large pre-trained neural language models, they are prone to generating toxic language, which brings security risks to their applications. We introduce MIL-Decoding, which detoxifies language models at token-level by interpolating it with a trained multiple instance learning (MIL) network.MIL model is trained on a corpus with a toxicity label for each text to predict the overall toxicity and the toxicity of each token in its context. Intuitively, the MIL network computes a toxicity distribution over next tokens according to the generated context which supplements the original language model to avoid toxicity. We evaluate MIL-Decoding with automatic metrics and human evaluation, where MIL-Decoding outperforms other baselines in detoxification while it only hurts generation fluency a little bit.
Dependency resolution at the syntax-semantics interface: psycholinguistic and computational insights on control dependencies
Authors: Iria de-Dios-Flores; Juan Garcia Amboage; Marcos Garcia
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Using psycholinguistic and computational experiments we compare the ability of humans and several pre-trained masked language models to correctly identify control dependencies in Spanish sentences such as ‘José le prometió/ordenó a María ser ordenado/a’ (‘Joseph promised/ordered Mary to be tidy’). These structures underlie complex anaphoric and agreement relations at the interface of syntax and semantics, allowing us to study lexically-guided antecedent retrieval processes. Our results show that while humans correctly identify the (un)acceptability of the strings, language models often fail to identify the correct antecedent in non-adjacent dependencies, showing their reliance on linearity. Additional experiments on Galician reinforce these conclusions. Our findings are equally valuable for the evaluation of language models’ ability to capture linguistic generalizations, as well as for psycholinguistic theories of anaphor resolution.
Open-ended Long Text Generation via Masked Language Modeling
Authors: Xiaobo Liang; Zecheng Tang; Juntao Li; Min Zhang
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Pre-trained autoregressive (AR) language models such as BART and GPTs have dominated OPen-ended Long Text Generation (Open-LTG).However, the AR nature will decrease the inference efficiency along with the increase of generation length, which hinder their application in Open-LTG.To improve inference efficiency, we alternatively explore the potential of the pre-trained masked language models (MLMs) along with a representative iterative non-autoregressive (NAR) decoding strategy for Open-LTG.Our preliminary study shows that pre-trained MLMs can merely generate short text and will collapse for long text modeling. To enhance the long text generation capability of MLMs, we introduce two simple yet effective strategies for the iterative NAR model: dynamic sliding window attention (DSWA) and linear temperature decay (LTD). It can alleviate long-distance collapse problems and achieve longer text generation with a flexible trade-off between performance and inference speedup. Experiments on the storytelling and multi-paragraph opinionated article writing tasks show that pre-trained MLMs can achieve more than 3 × → 13 × speedup with better performance than strong AR models.
A Method for Studying Semantic Construal in Grammatical Constructions with Interpretable Contextual Embedding Spaces
Authors: Gabriella Chronis; Kyle Mahowald; Katrin Erk
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: We study semantic construal in grammatical constructions using large language models. First, we project contextual word embeddings into three interpretable semantic spaces, each defined by a different set of psycholinguistic feature norms. We validate these interpretable spaces and then use them to automatically derive semantic characterizations of lexical items in two grammatical constructions: nouns in subject or object position within the same sentence, and the AANN construction (e.g., ‘a beautiful three days’). We show that a word in subject position is interpreted as more agentive than the very same word in object position, and that the nouns in the AANN construction are interpreted as more measurement-like than when in the canonical alternation. Our method can probe the distributional meaning of syntactic constructions at a templatic level, abstracted away from specific lexemes.
Holographic CCG Parsing
Authors: Ryosuke Yamaki; Tadahiro Taniguchi; Daichi Mochihashi
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: We propose a method for formulating CCG as a recursive composition in a continuous vector space. Recent CCG supertagging and parsing models generally demonstrate high performance, yet rely on black-box neural architectures to implicitly model phrase structure dependencies. Instead, we leverage the method of holographic embeddings as a compositional operator to explicitly model the dependencies between words and phrase structures in the embedding space. Experimental results revealed that holographic composition effectively improves the supertagging accuracy to achieve state-of-the-art parsing performance when using a C&C parser. The proposed span-based parsing algorithm using holographic composition achieves performance comparable to state-of-the-art neural parsing with Transformers. Furthermore, our model can semantically and syntactically infill text at the phrase level due to the decomposability of holographic composition.
Prompts Can Play Lottery Tickets Well: Achieving Lifelong Information Extraction via Lottery Prompt Tuning
Authors: Zujie Liang; Feng Wei; Yin Jie; Yuxi Qian; Zhenghong Hao; Bing Han
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Thanks to the recent success of Pre-trained Language Models (PLMs), it has become a promising research direction to develop a universal model (UIE) that can solve all typical information extraction tasks within one generative framework. Nonetheless, in real-world scenarios of UIE applications, new data of different IE tasks and domains usually come in a stream over time. A desirable UIE system should be capable of continually learning new tasks without forgetting old ones, thereby allowing knowledge and functionalities expansion without re-training the whole system. In this paper, we study the UIE system under a more challenging yet practical scenario, i.e., “lifelong learning” settings, to evaluate its abilities in three aspects, including knowledge sharing and expansion, catastrophic forgetting prevention, and rapid generalization on few-shot and unseen tasks. To achieve these three goals, we present a novel parameter- and deployment-efficient prompt tuning method namely Lottery Prompt Tuning (LPT).LPT freezes the PLM’s parameters and sequentially learns compact pruned prompt vectors for each task leveraging a binary prompt mask, while keeping the prompt parameters selected by the previous tasks insusceptible. Furthermore, we use a simple yet effective method to perform mask selection and show the powerful transferability of Lottery Prompts to novel tasks. Extensive experiments demonstrate that LPT consistently sets state-of-the-art performance on multiple lifelong learning settings of UIE, including task-incremental setting on seen tasks, few-shot adaptation, and zero-shot generalization on novel tasks.
Retrieve-and-Sample: Document-level Event Argument Extraction via Hybrid Retrieval Augmentation
Authors: Yubing Ren; Yanan Cao; Ping Guo; Fang Fang; Wei Ma; Zheng Lin
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Recent studies have shown the effectiveness of retrieval augmentation in many generative NLP tasks. These retrieval-augmented methods allow models to explicitly acquire prior external knowledge in a non-parametric manner and regard the retrieved reference instances as cues to augment text generation. These methods use similarity-based retrieval, which is based on a simple hypothesis: the more the retrieved demonstration resembles the original input, the more likely the demonstration label resembles the input label. However, due to the complexity of event labels and sparsity of event arguments, this hypothesis does not always hold in document-level EAE. This raises an interesting question: How do we design the retrieval strategy for document-level EAE? We investigate various retrieval settings from the input and label distribution views in this paper. We further augment document-level EAE with pseudo demonstrations sampled from event semantic regions that can cover adequate alternatives in the same context and event schema. Through extensive experiments on RAMS and WikiEvents, we demonstrate the validity of our newly introduced retrieval-augmented methods and analyze why they work.
WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning
Authors: Wenhao Wu; Wei Li; Xinyan Xiao; Jiachen Liu; Sujian Li; Yajuan Lyu
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: A crucial issue of current text generation models is that they often uncontrollably generate text that is factually inconsistent with inputs. Due to lack of annotated data, existing factual consistency metrics usually train evaluation models on synthetic texts or directly transfer from other related tasks, such as question answering (QA) and natural language inference (NLI).Bias in synthetic text or upstream tasks makes them perform poorly on text actually generated by language models, especially for general evaluation for various tasks. To alleviate this problem, we propose a weakly supervised framework named WeCheck that is directly trained on actual generated samples from language models with weakly annotated labels.WeCheck first utilizes a generative model to infer the factual labels of generated samples by aggregating weak labels from multiple resources.Next, we train a simple noise-aware classification model as the target metric using the inferred weakly supervised information.Comprehensive experiments on various tasks demonstrate the strong performance of WeCheck, achieving an average absolute improvement of 3.3% on the TRUE benchmark over 11B state-of-the-art methods using only 435M parameters.Furthermore, it is up to 30 times faster than previous evaluation methods, greatly improving the accuracy and efficiency of factual consistency evaluation.
AMR-based Network for Aspect-based Sentiment Analysis
Authors: Fukun Ma; Xuming Hu; Aiwei Liu; Yawen Yang; Shuang Li; Philip S. Yu; Lijie Wen
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task. Many recent works have used dependency trees to extract the relation between aspects and contexts and have achieved significant improvements. However, further improvement is limited due to the potential mismatch between the dependency tree as a syntactic structure and the sentiment classification as a semantic task. To alleviate this gap, we replace the syntactic dependency tree with the semantic structure named Abstract Meaning Representation (AMR) and propose a model called AMR-based Path Aggregation Relational Network (APARN) to take full advantage of semantic structures. In particular, we design the path aggregator and the relation-enhanced self-attention mechanism that complement each other. The path aggregator extracts semantic features from AMRs under the guidance of sentence information, while the relation-enhanced self-attention mechanism in turn improves sentence features with refined semantic information. Experimental results on four public datasets demonstrate 1.13% average F1 improvement of APARN in ABSA when compared with state-of-the-art baselines.
Text Adversarial Purification as Defense against Adversarial Attacks
Authors: Linyang Li; Demin Song; Xipeng Qiu
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Adversarial purification is a successful defense mechanism against adversarial attacks without requiring knowledge of the form of the incoming attack. Generally, adversarial purification aims to remove the adversarial perturbations therefore can make correct predictions based on the recovered clean samples. Despite the success of adversarial purification in the computer vision field that incorporates generative models such as energy-based models and diffusion models,using purification as a defense strategy against textual adversarial attacks is rarely explored. In this work, we introduce a novel adversarial purification method that focuses on defending against textual adversarial attacks. With the help of language models, we can inject noise by masking input texts and reconstructing the masked texts based on the masked language models. In this way, we construct an adversarial purification process for textual models against the most widely used word-substitution adversarial attacks. We test our proposed adversarial purification method on several strong adversarial attack methods including Textfooler and BERT-Attack and experimental results indicate that the purification algorithm can successfully defend against strong word-substitution attacks.
SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres
Authors: Shumin Deng; Shengyu Mao; Ningyu Zhang; Bryan Hooi
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Event-centric structured prediction involves predicting structured outputs of events. In most NLP cases, event structures are complex with manifold dependency, and it is challenging to effectively represent these complicated structured events. To address these issues, we propose Structured Prediction with Energy-based Event-Centric Hyperspheres (SPEECH). SPEECH models complex dependency among event structured components with energy-based modeling, and represents event classes with simple but effective hyperspheres. Experiments on two unified-annotated event datasets indicate that SPEECH is predominant in event detection and event-relation extraction tasks.
Rule By Example: Harnessing Logical Rules for Explainable Hate Speech Detection
Authors: Christopher Clarke; Matthew Hall; Gaurav Mittal; Ye Yu; Sandra Sajeev; Jason Mars; Mei Chen
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Classic approaches to content moderation typically apply a rule-based heuristic approach to flag content. While rules are easily customizable and intuitive for humans to interpret, they are inherently fragile and lack the flexibility or robustness needed to moderate the vast amount of undesirable content found online today. Recent advances in deep learning have demonstrated the promise of using highly effective deep neural models to overcome these challenges. However, despite the improved performance, these data-driven models lack transparency and explainability, often leading to mistrust from everyday users and a lack of adoption by many platforms. In this paper, we present Rule By Example (RBE): a novel exemplar-based contrastive learning approach for learning from logical rules for the task of textual content moderation. RBE is capable of providing rule-grounded predictions, allowing for more explainable and customizable predictions compared to typical deep learning-based approaches. We demonstrate that our approach is capable of learning rich rule embedding representations using only a few data examples. Experimental results on 3 popular hate speech classification datasets show that RBE is able to outperform state-of-the-art deep learning classifiers as well as the use of rules in both supervised and unsupervised settings while providing explainable model predictions via rule-grounding.
What about “em”? How Commercial Machine Translation Fails to Handle (Neo-)Pronouns
Authors: Anne Lauscher; Debora Nozza; Ehm Miltersen; Archie Crowley; Dirk Hovy
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: As 3rd-person pronoun usage shifts to include novel forms, e.g., neopronouns, we need more research on identity-inclusive NLP. Exclusion is particularly harmful in one of the most popular NLP applications, machine translation (MT). Wrong pronoun translations can discriminate against marginalized groups, e.g., non-binary individuals (Dev et al., 2021). In this “reality check”, we study how three commercial MT systems translate 3rd-person pronouns. Concretely, we compare the translations of gendered vs. gender-neutral pronouns from English to five other languages (Danish, Farsi, French, German, Italian), and vice versa, from Danish to English.Our error analysis shows that the presence of a gender-neutral pronoun often leads to grammatical and semantic translation errors. Similarly, gender neutrality is often not preserved. By surveying the opinions of affected native speakers from diverse languages, we provide recommendations to address the issue in future MT research.
What Is Overlap Knowledge in Event Argument Extraction? APE: A Cross-datasets Transfer Learning Model for EAE
Authors: Kaihang Zhang; Kai Shuang; Xinyue Yang; Xuyang Yao; Jinyu Guo
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: The EAE task extracts a structured event record from an event text. Most existing approaches train the EAE model on each dataset independently and ignore the overlap knowledge across datasets. However, insufficient event records in a single dataset often prevent the existing model from achieving better performance. In this paper, we clearly define the overlap knowledge across datasets and split the knowledge of the EAE task into overlap knowledge across datasets and specific knowledge of the target dataset. We propose APE model to learn the two parts of knowledge in two serial learning phases without causing catastrophic forgetting. In addition, we formulate both learning phases as conditional generation tasks and design Stressing Entity Type Prompt to close the gap between the two phases. The experiments show APE achieves new state-of-the-art with a large margin in the EAE task. When only ten records are available in the target dataset, our model dramatically outperforms the baseline model with average 27.27% F1 gain.
Tailor: A Soft-Prompt-Based Approach to Attribute-Based Controlled Text Generation
Authors: Kexin Yang; Dayiheng Liu; Wenqiang Lei; Baosong Yang; Mingfeng Xue; Boxing Chen; Jun Xie
Venue: ACL 2023 (Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers))

Abstract: Attribute-based Controlled Text Generation (CTG) refers to generating sentences that satisfy desirable attributes (e.g., emotions and topics). Existing work usually utilize fine-tuning or resort to extra attribute classifiers, yet suffer from increases in storage and inference time. To address these concerns, we explore attribute-based CTG in a parameter-efficient manner. In short, the proposed Tailor represents each attribute as a pre-trained continuous vector i.e., single-attribute prompt), which guides the generation of a fixed pre-trained language model (PLM) to satisfy a pre-specified attribute. These prompts can be simply concatenated as a whole for multi-attribute CTG without any re-training. Nevertheless, this may raise problems of fluency downgrading and position sensitivity. To solve this, Tailor provides two solutions to enhance the combination. The former contains a multi-attribute prompt mask and a re-indexing position sequence to bridge the gap between the training (one single-attribute prompt for each task) and the testing stage (concatenating two prompts). The latter introduces a trainable prompt connector to further enhance the combinations. Experiments demonstrate that, only requiring 0.08% extra training parameters of the GPT-2, Tailor can achieve effective and general improvements on eleven attribute-specific generation tasks.