GenAI4ELab/papercli-papers huggingface dataset:
Search-Oriented Conversational Query Editing
Authors: Kelong Mao; Zhicheng Dou; Bang Liu; Hongjin Qian; Fengran Mo; Xiangli Wu; Xiaohua Cheng; Zhao Cao
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Conversational query rewriting (CQR) realizes conversational search by reformulating the search dialogue into a standalone rewrite. However, existing CQR models either are not learned toward improving the downstream search performance or inefficiently generate the rewrite token-by-token from scratch while neglecting the fact that the search dialogue often has a large overlap with the rewrite. In this paper, we propose EdiRCS, a new text editing-based CQR model tailored for conversational search. In EdiRCS, most of the rewrite tokens are selected from the dialogue in a non-autoregressive fashion and only a few new tokens are generated to supplement the final rewrite, which makes EdiRCS highly efficient. In particular, the learning of EdiRCS is augmented with two search-oriented objectives, including contrastive ranking augmentation and contextualization knowledge transfer, which effectively improve it to select and generate more useful tokens from the view of retrieval. We show that EdiRCS outperforms state-of-the-art CQR models on three conversational search benchmarks while having low rewriting latency, and is robust to out-of-domain search dialogues and long dialogue contexts.
TAPIR: Learning Adaptive Revision for Incremental Natural Language Understanding with a Two-Pass Model
Authors: Patrick Kahardipraja; Brielen Madureira; David Schlangen
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Language is by its very nature incremental in how it is produced and processed. This property can be exploited by NLP systems to produce fast responses, which has been shown to be beneficial for real-time interactive applications. Recent neural network-based approaches for incremental processing mainly use RNNs or Transformers. RNNs are fast but monotonic (cannot correct earlier output, which can be necessary in incremental processing). Transformers, on the other hand, consume whole sequences, and hence are by nature non-incremental. A restart-incremental interface that repeatedly passes longer input prefixes can be used to obtain partial outputs, while providing the ability to revise. However, this method becomes costly as the sentence grows longer. In this work, we propose the Two-pass model for AdaPtIve Revision (TAPIR) and introduce a method to obtain an incremental supervision signal for learning an adaptive revision policy. Experimental results on sequence labelling show that our model has better incremental performance and faster inference speed compared to restart-incremental Transformers, while showing little degradation on full sequences.
Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind
Authors: Ece Takmaz; Nicolo’ Brandizzi; Mario Giulianelli; Sandro Pezzelle; Raquel Fernandez
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Dialogue participants may have varying levels of knowledge about the topic under discussion. In such cases, it is essential for speakers to adapt their utterances by taking their audience into account. Yet, it is an open question how such adaptation can be modelled in computational agents. In this paper, we model a visually grounded referential game between a knowledgeable speaker and a listener with more limited visual and linguistic experience. Inspired by psycholinguistic theories, we endow our speaker with the ability to adapt its referring expressions via a simulation module that monitors the effectiveness of planned utterances from the listener’s perspective. We propose an adaptation mechanism building on plug-and-play approaches to controlled language generation, where utterance generation is steered on the fly by the simulator without finetuning the speaker’s underlying language model. Our results and analyses show that our approach is effective: the speaker’s utterances become closer to the listener’s domain of expertise, which leads to higher communicative success.
A Semi-Autoregressive Graph Generative Model for Dependency Graph Parsing
Authors: Ye Ma; Mingming Sun; Ping Li
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Recent years have witnessed the impressive progress in Neural Dependency Parsing. According to the different factorization approaches to the graph joint probabilities, existing parsers can be roughly divided into autoregressive and non-autoregressive patterns. The former means that the graph should be factorized into multiple sequentially dependent components, then it can be built up component by component. And the latter assumes these components to be independent so that they can be outputted in a one-shot manner. However, when treating the directed edge as an explicit dependency relationship, we discover that there is a mixture of independent and interdependent components in the dependency graph, signifying that both aforementioned models fail to precisely capture the explicit dependencies among nodes and edges. Based on this property, we design a Semi-Autoregressive Dependency Parser to generate dependency graphs via adding node groups and edge groups autoregressively while pouring out all group elements in parallel. The model gains a trade-off between non-autoregression and autoregression, which respectively suffer from the lack of target inter-dependencies and the uncertainty of graph generation orders. The experiments show the proposed parser outperforms strong baselines on Enhanced Universal Dependencies of multiple languages, especially achieving 4% average promotion at graph-level accuracy. Also, the performances of model variations show the importance of specific parts.
AMR-TST: Abstract Meaning Representation-based Text Style Transfer
Authors: Kaize Shi; Xueyao Sun; Li He; Dingxian Wang; Qing Li; Guandong Xu
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Abstract Meaning Representation (AMR) is a semantic representation that can enhance natural language generation (NLG) by providing a logical semantic input. In this paper, we propose the AMR-TST, an AMR-based text style transfer (TST) technique. The AMR-TST converts the source text to an AMR graph and generates the transferred text based on the AMR graph modified by a TST policy named style rewriting. Our method combines both the explainability and diversity of explicit and implicit TST methods. The experiments show that the proposed method achieves state-of-the-art results compared with other baseline models in automatic and human evaluations. The generated transferred text in qualitative evaluation proves the AMR-TST have significant advantages in keeping semantic features and reducing hallucinations. To the best of our knowledge, this work is the first to apply the AMR method focusing on node-level features to the TST task.
Understanding the Cooking Process with English Recipe Text
Authors: Yi Fan; Anthony Hunter
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Translating procedural text, like recipes, into a graphical representation can be important for visualizing the text, and can offer a machine-readable formalism for use in software. There are proposals for translating recipes into a flow graph representation, where each node represents an ingredient, action, location, or equipment, and each arc between the nodes denotes the steps of the recipe. However, these proposals have had performance problems with both named entity recognition and relationship extraction. To address these problems, we propose a novel framework comprising two modules to construct a flow graph from the input recipe. The first module identifies the named entities in the input recipe text using BERT, Bi-LSTM and CRF, and the second module uses BERT to predict the relationships between the entities. We evaluate our framework on the English recipe flow graph corpus. Our framework can predict the edge label and achieve the overall F1 score of 92.2, while the baseline F1 score is 43.3 without the edge label predicted.
Follow the Wisdom of the Crowd: Effective Text Generation via Minimum Bayes Risk Decoding
Authors: Mirac Suzgun; Luke Melas-Kyriazi; Dan Jurafsky
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: In open-ended natural-language generation, existing text decoding methods typically struggle to produce text which is both diverse and high-quality. Greedy and beam search are known to suffer from text degeneration and linguistic diversity issues, while temperature, top-k, and nucleus sampling yield diverse but often lower-quality outputs. In this work, we build upon Minimum Bayes Risk Decoding (MBRD), a family of decoding methods based on Bayesian risk minimization, to address this diversity-quality trade-off. Inspired by the principle of the wisdom of the crowd, MBRD seeks to select a candidate from a pool of candidates that has the least expected risk under a generative model according to a given utility function. The crowd of candidates serves as an approximation for the distribution over human-generated references. We show that MBRD generalizes numerous decoding methods, including majority voting, and can be used as a drop-in replacement for existing sampling methods. Across a wide range of tasks—such as summarization, data-to-text, translation, and textual style transfer—MBRD yields 3-7 ROUGE and BLEU point improvements, including state-of-the-art results on WebNLG and WMT’16.
RobustQA: Benchmarking the Robustness of Domain Adaptation for Open-Domain Question Answering
Authors: Rujun Han; Peng Qi; Yuhao Zhang; Lan Liu; Juliette Burger; William Yang Wang; Zhiheng Huang; Bing Xiang; Dan Roth
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Open-domain question answering (ODQA) is a crucial task in natural language processing. A typical ODQA system relies on a retriever module to select relevant contexts from a large corpus for a downstream reading comprehension model. Existing ODQA datasets consist mainly of Wikipedia corpus, and are insufficient to study models’ generalizability across diverse domains as models are trained and evaluated on the same genre of data. We propose **RobustQA**, a novel benchmark consisting of datasets from 8 different domains, which facilitates the evaluation of ODQA’s domain robustness. To build **RobustQA**, we annotate QA pairs in retrieval datasets with rigorous quality control. We further examine improving QA performances by incorporating unsupervised learning methods with target-domain corpus and adopting large generative language models. These methods can effectively improve model performances on **RobustQA**. However, experimental results demonstrate a significant gap from in-domain training, suggesting that **RobustQA** is a challenging benchmark to evaluate ODQA domain robustness.
SenteCon: Leveraging Lexicons to Learn Human-Interpretable Language Representations
Authors: Victoria Lin; Louis-Philippe Morency
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Although deep language representations have become the dominant form of language featurization in recent years, in many settings it is important to understand a model’s decision-making process. This necessitates not only an interpretable model but also interpretable features. In particular, language must be featurized in a way that is interpretable while still characterizing the original text well. We present SenteCon, a method for introducing human interpretability in deep language representations. Given a passage of text, SenteCon encodes the text as a layer of interpretable categories in which each dimension corresponds to the relevance of a specific category. Our empirical evaluations indicate that encoding language with SenteCon provides high-level interpretability at little to no cost to predictive performance on downstream tasks. Moreover, we find that SenteCon outperforms existing interpretable language representations with respect to both its downstream performance and its agreement with human characterizations of the text.
Reinforcement Learning for Topic Models
Authors: Jeremy Costello; Marek Reformat
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: We apply reinforcement learning techniques to topic modeling by replacing the variational autoencoder in ProdLDA with a continuous action space reinforcement learning policy. We train the system with a policy gradient algorithm REINFORCE. Additionally, we introduced several modifications: modernize the neural network architecture, weight the ELBO loss, use contextual embeddings, and monitor the learning process via computing topic diversity and coherence for each training step. Experiments areperformed on 11 data sets. Our unsupervised model outperforms all other unsupervised models and performs on par with or better than most models using supervised labeling. Our model is outperformed on certain data sets by a model using supervised labeling and contrastive learning. We have also conducted an ablation study to provide empirical evidence of performance improvements from changes we made to ProdLDA and found that the reinforcement learning formulation boosts performance. We open-source our code implementation.
Contextualized Soft Prompts for Extraction of Event Arguments
Authors: Chien Nguyen; Hieu Man; Thien Nguyen
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Event argument extraction (EAE) is a sub-task of event extraction where the goal is to identify roles of entity mentions for events in text. The current state-of-the-art approaches for this problem explore prompt-based methods to prompt pre-trained language models for arguments over input context. However, existing prompt-based methods mainly rely on discrete and manually-designed prompts that cannot exploit specific context for each example to improve customization for optimal performance. In addition, the discrete nature of current prompts prevents the incorporation of relevant context from multiple external documents to enrich prompts for EAE. To this end, we propose a novel prompt-based method for EAE that introduces soft prompts to facilitate the encoding of individual example context and multiple relevant documents to boost EAE. We extensively evaluate the proposed method on benchmark datasets for EAE to demonstrate its benefits with state-of-the-art performance.
TextVerifier: Robustness Verification for Textual Classifiers with Certifiable Guarantees
Authors: Siqi Sun; Wenjie Ruan
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: When textual classifiers are deployed in safety-critical workflows, they must withstand the onslaught of AI-enabled model confusion caused by adversarial examples with minor alterations. In this paper, the main objective is to provide a formal verification framework, called TextVerifier, with certifiable guarantees on deep neural networks in natural language processing against word-level alteration attacks. We aim to provide an approximation of the maximal safe radius by deriving provable bounds both mathematically and automatically, where a minimum word-level L_0 distance is quantified as a guarantee for the classification invariance of victim models. Here, we illustrate three strengths of our strategy: i) certifiable guarantee: effective verification with convergence to ensure approximation of maximal safe radius with tight bounds ultimately; ii) high-efficiency: it yields an efficient speed edge by a novel parallelization strategy that can process a set of candidate texts simultaneously on GPUs; and iii) reliable anytime estimation: the verification can return intermediate bounds, and robustness estimates that are gradually, but strictly, improved as the computation proceeds. Furthermore, experiments are conducted on text classification on four datasets over three victim models to demonstrate the validity of tightening bounds. Our tool TextVerifier is available at https://github.com/TrustAI/TextVerifer.
OASum: Large-Scale Open Domain Aspect-based Summarization
Authors: Xianjun Yang; Kaiqiang Song; Sangwoo Cho; Xiaoyang Wang; Xiaoman Pan; Linda Petzold; Dong Yu
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Aspect or query-based summarization has recently caught more attention, as it can generate differentiated summaries based on users’ interests. However, the current dataset for aspect or query-based summarization either focuses on specific domains, on a relatively small scale, or contains only a few aspect types. Such limitations hinder further explorations in this direction. In this work, we take advantage of crowd-sourcing knowledge on Wikipedia and automatically create a high-quality, large-scale open-domain aspect-based summarization dataset named OASum, which contains more than 3.7 million instances with around 1 million different aspects on 2 million Wikipedia pages. We provide benchmark results on OASum and demonstrate its ability for diverse aspect-based summarization generation. To overcome the data scarcity problem on specific domains, we also perform zero-shot, few-shot, and fine-tuning on seven downstream datasets. Specifically, zero/few-shot and fine-tuning results show that the model pre-trained on our corpus demonstrates a strong aspect or query-focused generation ability compared with the backbone model. Our dataset and pre-trained checkpoints are publicly available.
On the Limitations of Simulating Active Learning
Authors: Katerina Margatina; Nikolaos Aletras
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Active learning (AL) is a human-and-model-in-the-loop paradigm that iteratively selects informative unlabeled data for human annotation, aiming to improve data efficiency over random sampling. However, performing AL experiments with human annotations on-the-fly is a laborious and expensive process, thus unrealistic for academic research. An easy fix to this impediment is to simulate AL, by treating an already labeled and publicly available dataset as the pool of unlabeled data. In this position paper, we first survey recent literature and highlight the challenges across all different steps within the AL loop. We further unveil neglected caveats in the experimental setup that can significantly affect the quality of AL research. We continue with an exploration of how the simulation setting can govern empirical findings, arguing that it might be one of the answers behind the ever posed question “Why do Active Learning algorithms sometimes fail to outperform random sampling?”. We argue that evaluating AL algorithms on available labeled datasets might provide a lower bound as to their effectiveness in real data. We believe it is essential to collectively shape the best practices for AL research, especially now that the stellar engineering advances (e.g. ChatGPT) shift the research focus to data-driven approaches. To this end, we present guidelines for future work, hoping that by bringing these limitations to the community’s attention, we can explore ways to address them.
Towards Alleviating the Object Bias in Prompt Tuning-based Factual Knowledge Extraction
Authors: Yuhang Wang; Dongyuan Lu; Chao Kong; Jitao Sang
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Many works employed prompt tuning methods to automatically optimize prompt queries and extract the factual knowledge stored in Pre-trained Language Models. In this paper, we observe that the optimized prompts, including discrete prompts and continuous prompts, exhibit undesirable object bias. To handle this problem, we propose a novel prompt tuning method called MeCoD consisting of three modules: Prompt Encoder, Object Equalization and Biased Object Obstruction. Experimental results show that MeCoD can significantly reduce the object bias and at the same time improve accuracy of factual knowledge extraction.
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport
Authors: Weijie Xu; Xiaoyu Jiang; Srinivasan Sengamedu Hanumantha Rao; Francis Iannacci; Jinjin Zhao
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Recently, Neural Topic Models (NTM), inspired by variational autoencoders, have attracted a lot of research interest; however, these methods have limited applications in the real world due to the challenge of incorporating human knowledge. This work presents a semi-supervised neural topic modeling method, vONTSS, which uses von Mises-Fisher (vMF) based variational autoencoders and optimal transport. When a few keywords per topic are provided, vONTSS in the semi-supervised setting generates potential topics and optimizes topic-keyword quality and topic classification. Experiments show that vONTSS outperforms existing semi-supervised topic modeling methods in classification accuracy and diversity. vONTSS also supports unsupervised topic modeling. Quantitative and qualitative experiments show that vONTSS in the unsupervised setting outperforms recent NTMs on multiple aspects: vONTSS discovers highly clustered and coherent topics on benchmark datasets. It is also much faster than the state-of-the-art weakly supervised text classification method while achieving similar classification performance. We further prove the equivalence of optimal transport loss and cross-entropy loss at the global minimum.
Bias Beyond English: Counterfactual Tests for Bias in Sentiment Analysis in Four Languages
Authors: Seraphina Goldfarb-Tarrant; Adam Lopez; Roi Blanco; Diego Marcheggiani
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Sentiment analysis (SA) systems are used in many products and hundreds of languages. Gender and racial biases are well-studied in English SA systems, but understudied in other languages, with few resources for such studies. To remedy this, we build a counterfactual evaluation corpus for gender and racial/migrant bias in four languages. We demonstrate its usefulness by answering a simple but important question that an engineer might need to answer when deploying a system: What biases do systems import from pre-trained models when compared to a baseline with no pre-training? Our evaluation corpus, by virtue of being counterfactual, not only reveals which models have less bias, but also pinpoints changes in model bias behaviour, which enables more targeted mitigation strategies. We release our code and evaluation corpora to facilitate future research.