GenAI4ELab/papercli-papers huggingface dataset:
Focus-aware Response Generation in Inquiry Conversation
Authors: Yiquan Wu; Weiming Lu; Yating Zhang; Adam Jatowt; Jun Feng; Changlong Sun; Fei Wu; Kun Kuang
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Inquiry conversation is a common form of conversation that aims to complete the investigation (e.g., court hearing, medical consultation and police interrogation) during which a series of focus shifts occurs. While many models have been proposed to generate a smooth response to a given conversation history, neglecting the focus can limit performance in inquiry conversation where the order of the focuses plays there a key role. In this paper, we investigate the problem of response generation in inquiry conversation by taking the focus into consideration. We propose a novel Focus-aware Response Generation (FRG) method by jointly optimizing a multi-level encoder and a set of focal decoders to generate several candidate responses that correspond to different focuses. Additionally, a focus ranking module is proposed to predict the next focus and rank the candidate responses. Experiments on two orthogonal inquiry conversation datasets (judicial, medical domain) demonstrate that our method generates results significantly better in automatic metrics and human evaluation compared to the state-of-the-art approaches.
A Hierarchical Explanation Generation Method Based on Feature Interaction Detection
Authors: Yiming Ju; Yuanzhe Zhang; Kang Liu; Jun Zhao
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: The opaqueness of deep NLP models has motivated efforts to explain how deep models predict. Recently, work has introduced hierarchical attribution explanations, which calculate attribution scores for compositional text hierarchically to capture compositional semantics. Existing work on hierarchical attributions tends to limit the text groups to a continuous text span, which we call the connecting rule. While easy for humans to read, limiting the attribution unit to a continuous span might lose important long-distance feature interactions for reflecting model predictions. In this work, we introduce a novel strategy for capturing feature interactions and employ it to build hierarchical explanations without the connecting rule. The proposed method can convert ubiquitous non-hierarchical explanations (e.g., LIME) into their corresponding hierarchical versions. Experimental results show the effectiveness of our approach in building high-quality hierarchical explanations.
Jointly Reparametrized Multi-Layer Adaptation for Efficient and Private Tuning
Authors: Umang Gupta; Aram Galstyan; Greg Ver Steeg
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Efficient finetuning of pretrained language transformers is becoming increasingly prevalent for solving natural language processing tasks. While effective, it can still require a large number of tunable parameters. This can be a drawback for low-resource applications and training with differential-privacy constraints, where excessive noise may be introduced during finetuning. To this end, we propose a novel language transformer finetuning strategy that introduces task-specific parameters in multiple transformer layers. These parameters are derived from fixed random projections of a single trainable vector, enabling finetuning with significantly fewer parameters while maintaining performance. We achieve within 5% of full finetuning performance on GLUE tasks with as few as 4,100 parameters per task, outperforming other parameter-efficient finetuning approaches that use a similar number of per-task parameters. Besides, the random projections can be precomputed at inference, avoiding additional computational latency. All these make our method particularly appealing for low-resource applications. Finally, our method achieves the best or comparable utility compared to several recent finetuning methods when training with the same privacy constraints, underscoring its effectiveness and potential real-world impact.
A Diffusion Model for Event Skeleton Generation
Authors: Fangqi Zhu; Lin Zhang; Jun Gao; Bing Qin; Ruifeng Xu; Haiqin Yang
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Event skeleton generation, aiming to induce an event schema skeleton graph with abstracted event nodes and their temporal relations from a set of event instance graphs, is a critical step in the temporal complex event schema induction task. Existing methods effectively address this task from a graph generation perspective but suffer from noise-sensitive and error accumulation, e.g., the inability to correct errors while generating schema. We, therefore, propose a novel Diffusion Event Graph Model (DEGM) to address these issues. Our DEGM is the first workable diffusion model for event skeleton generation, where the embedding and rounding techniques with a custom edge-based loss are introduced to transform a discrete event graph into learnable latent representations. Furthermore, we propose a denoising training process to maintain the model’s robustness. Consequently, DEGM derives the final schema, where error correction is guaranteed by iteratively refining the latent representations during the schema generation process. Experimental results on three IED bombing datasets demonstrate that our DEGM achieves better results than other state-of-the-art baselines. Our code and data are available at https://github.com/zhufq00/EventSkeletonGeneration.
Nonparametric Decoding for Generative Retrieval
Authors: Hyunji Lee; JaeYoung Kim; Hoyeon Chang; Hanseok Oh; Sohee Yang; Vladimir Karpukhin; Yi Lu; Minjoon Seo
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: The generative retrieval model depends solely on the information encoded in its model parameters without external memory, its information capacity is limited and fixed. To overcome the limitation, we propose Nonparametric Decoding (Np Decoding) which can be applied to existing generative retrieval models. Np Decoding uses nonparametric contextualized vocab embeddings (external memory) rather than vanilla vocab embeddings as decoder vocab embeddings. By leveraging the contextualized vocab embeddings, the generative retrieval model is able to utilize both the parametric and nonparametric space. Evaluation over 9 datasets (8 single-hop and 1 multi-hop) in the document retrieval task shows that applying Np Decoding to generative retrieval models significantly improves the performance. We also show that Np Decoding is data- and parameter-efficient, and shows high performance in the zero-shot setting.
Aspect-aware Unsupervised Extractive Opinion Summarization
Authors: Haoyuan Li; Somnath Basu Roy Chowdhury; Snigdha Chaturvedi
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Extractive opinion summarization extracts sentences from users’ reviews to represent the prevalent opinions about a product or service. However, the extracted sentences can be redundant and may miss some important aspects, especially for centroid-based extractive summarization models (Radev et al., 2004). To alleviate these issues, we introduce TokenCluster– a method for unsupervised extractive opinion summarization that automatically identifies the aspects described in the review sentences and then extracts sentences based on their aspects. It identifies the underlying aspects of the review sentences using roots of noun phrases and adjectives appearing in them. Empirical evaluation shows that TokenCluster improves aspect coverage in summaries and achieves strong performance on multiple opinion summarization datasets, for both general and aspect-specific summarization. We also perform extensive ablation and human evaluation studies to validate the design choices of our method. The implementation of our work is available at https://github.com/leehaoyuan/TokenCluster
GNN-SL: Sequence Labeling Based on Nearest Examples via GNN
Authors: Shuhe Wang; Yuxian Meng; Rongbin Ouyang; Jiwei Li; Tianwei Zhang; Lingjuan Lyu; Guoyin Wang
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: To better handle long-tail cases in the sequence labeling (SL) task, in this work, we introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla SL model output with similar tagging examples retrieved from the whole training set. Since not all the retrieved tagging examples benefit the model prediction, we construct a heterogeneous graph, and leverage graph neural networks (GNNs) to transfer information between the retrieved tagging examples and the input word sequence. The augmented node which aggregates information from neighbors is used to do prediction. This strategy enables the model to directly acquire similar tagging examples and improves the general quality of predictions. We conduct a variety of experiments on three typical sequence labeling tasks: Named Entity Recognition (NER), Part of Speech Tagging (POS), and Chinese Word Segmentation (CWS) to show the significant performance of our GNN-SL. Notably, GNN-SL achieves SOTA results of 96.9 (+0.2) on PKU, 98.3 (+0.4) on CITYU, 98.5 (+0.2) on MSR, and 96.9 (+0.2) on AS for the CWS task, and resultscomparable to SOTA performances on NER datasets, and POS datasets.
Serial Contrastive Knowledge Distillation for Continual Few-shot Relation Extraction
Authors: Xinyi Wang; Zitao Wang; Wei Hu
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Continual few-shot relation extraction (RE) aims to continuously train a model for new relations with few labeled training data, of which the major challenges are the catastrophic forgetting of old relations and the overfitting caused by data sparsity. In this paper, we propose a new model, namely SCKD, to accomplish the continual few-shot RE task. Specifically, we design serial knowledge distillation to preserve the prior knowledge from previous models and conduct contrastive learning with pseudo samples to keep the representations of samples in different relations sufficiently distinguishable. Our experiments on two benchmark datasets validate the effectiveness of SCKD for continual few-shot RE and its superiority in knowledge transfer and memory utilization over state-of-the-art models.
Revisiting the Architectures like Pointer Networks to Efficiently Improve the Next Word Distribution, Summarization Factuality, and Beyond
Authors: Haw-Shiuan Chang; Zonghai Yao; Alolika Gon; Hong Yu; Andrew McCallum
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Is the output softmax layer, which is adopted by most language models (LMs), always the best way to compute the next word probability? Given so many attention layers in a modern transformer-based LM, are the pointer networks redundant nowadays? In this study, we discover that the answers to both questions are no. This is because the softmax bottleneck sometimes prevents the LMs from predicting the desired distribution and the pointer networks can be used to break the bottleneck efficiently. Based on the finding, we propose several softmax alternatives by simplifying the pointer networks and accelerating the word-by-word rerankers. In GPT-2, our proposals are significantly better and more efficient than mixture of softmax, a state-of-the-art softmax alternative. In summarization experiments, without very significantly decreasing its training/testing speed, our best method based on T5-Small improves factCC score by 2 points in CNN/DM and XSUM dataset, and improves MAUVE scores by 30% in BookSum paragraph-level dataset.
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-Distribution Generalization Perspective
Authors: Linyi Yang; Shuibai Zhang; Libo Qin; Yafu Li; Yidong Wang; Hanmeng Liu; Jindong Wang; Xing Xie; Yue Zhang
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Pre-trained language models (PLMs) are known to improve the generalization performance of natural language understanding models by leveraging large amounts of data during the pre-training phase. However, the out-of-distribution (OOD) generalization problem remains a challenge in many NLP tasks, limiting the real-world deployment of these methods. This paper presents the first attempt at creating a unified benchmark named GLUE-X for evaluating OOD robustness in NLP models, highlighting the importance of OOD robustness and providing insights on how to measure the robustness of a model and how to improve it. The benchmark includes 13 publicly available datasets for OOD testing, and evaluations are conducted on 8 classic NLP tasks over 21 popularly used PLMs. Our findings confirm the need for improved OOD accuracy in NLP tasks, as significant performance degradation was observed in all settings compared to in-distribution (ID) accuracy.
Investigating the Saliency of Sentiment Expressions in Aspect-Based Sentiment Analysis
Authors: Joachim Wagner; Jennifer Foster
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: We examine the behaviour of an aspect-based sentiment classifier built by fine-tuning the BERT BASE model on the SemEval 2016 English dataset. In a set of masking experiments, we examine the extent to which the tokens identified as salient by LIME and a gradient-based method are being used by the classifier. We find that both methods are able to produce faithful rationales, with LIME outperforming the gradient-based method. We also identify a set of manually annotated sentiment expressions for this dataset, and carry out more masking experiments with these as human rationales. The enhanced performance of a classifier that only sees the relevant sentiment expressions suggests that they are not being used to their full potential. A comparison of the LIME and gradient rationales with the sentiment expressions reveals only a moderate level of agreement. Some disagreements are related to the fixed length of the rationales and the tendency of the rationales to contain content words related to the aspect itself.
DMLM: Descriptive Masked Language Modeling
Authors: Edoardo Barba; Niccolò Campolungo; Roberto Navigli
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Over the last few years, Masked Language Modeling (MLM) pre-training has resulted in remarkable advancements in many Natural Language Understanding (NLU) tasks, which sparked an interest in researching alternatives and extensions to the MLM objective. In this paper, we tackle the absence of explicit semantic grounding in MLM and propose Descriptive Masked Language Modeling (DMLM), a knowledge-enhanced reading comprehension objective, where the model is required to predict the most likely word in a context, being provided with the word’s definition. For instance, given the sentence “I was going to the _”, if we provided as definition “financial institution”, the model would have to predict the word “bank”; if, instead, we provided “sandy seashore”, the model should predict “beach”. Our evaluation highlights the effectiveness of DMLM in comparison with standard MLM, showing improvements on a number of well-established NLU benchmarks, as well as other semantics-focused tasks, e.g., Semantic Role Labeling. Furthermore, we demonstrate how it is possible to take full advantage of DMLM to embed explicit semantics in downstream tasks, explore several properties of DMLM-based contextual representations and suggest a number of future directions to investigate.
Reproducibility in NLP: What Have We Learned from the Checklist?
Authors: Ian Magnusson; Noah A. Smith; Jesse Dodge
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Scientific progress in NLP rests on the reproducibility of researchers’ claims. The *CL conferences created the NLP Reproducibility Checklist in 2020 to be completed by authors at submission to remind them of key information to include. We provide the first analysis of the Checklist by examining 10,405 anonymous responses to it. First, we find evidence of an increase in reporting of information on efficiency, validation performance, summary statistics, and hyperparameters after the Checklist’s introduction. Further, we show acceptance rate grows for submissions with more Yes responses. We find that the 44% of submissions that gather new data are 5% less likely to be accepted than those that did not; the average reviewer-rated reproducibility of these submissions is also 2% lower relative to the rest. We find that only 46% of submissions claim to open-source their code, though submissions that do have 8% higher reproducibility score relative to those that do not, the most for any item. We discuss what can be inferred about the state of reproducibility in NLP, and provide a set of recommendations for future conferences, including: a) allowing submitting code and appendices one week after the deadline, and b) measuring dataset reproducibility by a checklist of data collection practices.
Domain Generalization via Switch Knowledge Distillation for Robust Review Representation
Authors: You Zhang; Jin Wang; Liang-Chih Yu; Dan Xu; Xuejie Zhang
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Applying neural models injected with in-domain user and product information to learn review representations of unseen or anonymous users incurs an obvious obstacle in content-based recommender systems. For the generalization of the in-domain classifier, most existing models train an extra plain-text model for the unseen domain. Without incorporating historical user and product information, such a schema makes unseen and anonymous users dissociate from the recommender system. To simultaneously learn the review representation of both existing and unseen users, this study proposed a switch knowledge distillation for domain generalization. A generalization-switch (GSwitch) model was initially applied to inject user and product information by flexibly encoding both domain-invariant and domain-specific features. By turning the status ON or OFF, the model introduced a switch knowledge distillation to learn a robust review representation that performed well for either existing or anonymous unseen users. The empirical experiments were conducted on IMDB, Yelp-2013, and Yelp-2014 by masking out users in test data as unseen and anonymous users. The comparative results indicate that the proposed method enhances the generalization capability of several existing baseline models. For reproducibility, the code for this paper is available at: https://github.com/yoyo-yun/DG_RRR.
On Search Strategies for Document-Level Neural Machine Translation
Authors: Christian Herold; Hermann Ney
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Compared to sentence-level systems, document-level neural machine translation (NMT) models produce a more consistent output across a document and are able to better resolve ambiguities within the input. There are many works on document-level NMT, mostly focusing on modifying the model architecture or training strategy to better accommodate the additional context-input. On the other hand, in most works, the question on how to perform search with the trained model is scarcely discussed, sometimes not mentioned at all. In this work, we aim to answer the question how to best utilize a context-aware translation model in decoding. We start with the most popular document-level NMT approach and compare different decoding schemes, some from the literature and others proposed by us. In the comparison, we are using both, standard automatic metrics, as well as specific linguistic phenomena on three standard document-level translation benchmarks. We find that most commonly used decoding strategies perform similar to each other and that higher quality context information has the potential to further improve the translation.
Causal Intervention for Mitigating Name Bias in Machine Reading Comprehension
Authors: Jiazheng Zhu; Shaojuan Wu; Xiaowang Zhang; Yuexian Hou; Zhiyong Feng
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Machine Reading Comprehension (MRC) is to answer questions based on a given passage, which has made great achievements using pre-trained Language Models (LMs). We study the robustness of MRC models to names which is flexible and repeatability. MRC models based on LMs may overuse the name information to make predictions, which causes the representation of names to be non-interchangeable, called name bias. In this paper, we propose a novel Causal Interventional paradigm for MRC (CI4MRC) to mitigate name bias. Specifically, we uncover that the pre-trained knowledge concerning names is indeed a confounder by analyzing the causalities among the pre-trained knowledge, context representation and answers based on a Structural Causal Model (SCM). We develop effective CI4MRC algorithmic implementations to constrain the confounder based on the neuron-wise and token-wise adjustments. Experiments demonstrate that our proposed CI4MRC effectively mitigates the name bias and achieves competitive performance on the original SQuAD. Moreover, our method is general to various pre-trained LMs and performs robustly on the adversarial datasets.
Counterfactual Probing for the Influence of Affect and Specificity on Intergroup Bias
Authors: Venkata Subrahmanyan Govindarajan; David Beaver; Kyle Mahowald; Junyi Jessy Li
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: While existing work on studying bias in NLP focues on negative or pejorative language use, Govindarajan et al. (2023) offer a revised framing of bias in terms of intergroup social context, and its effects on language behavior. In this paper, we investigate if two pragmatic features (specificity and affect) systematically vary in different intergroup contexts — thus connecting this new framing of bias to language output. Preliminary analysis finds modest correlations between specificity and affect of tweets with supervised intergroup relationship (IGR) labels. Counterfactual probing further reveals that while neural models finetuned for predicting IGR reliably use affect in classification, the model’s usage of specificity is inconclusive.
SongRewriter: A Chinese Song Rewriting System with Controllable Content and Rhyme Scheme
Authors: Yusen Sun; Liangyou Li; Qun Liu; Dit-Yan Yeung
Venue: ACL 2023 (Findings of the Association for Computational Linguistics: ACL 2023)

Abstract: Although lyrics generation has achieved significant progress in recent years, it has limited practical applications because the generated lyrics cannot be performed without composing compatible melodies. In this work, we bridge this practical gap by proposing a song rewriting system which rewrites the lyrics of an existing song such that the generated lyrics are compatible with the rhythm of the existing melody and thus singable. In particular, we propose SongRewriter, a controllable Chinese lyric generation and editing system which assists users without prior knowledge of melody composition. The system is trained by a randomized multi-level masking strategy which produces a unified model for generating entirely new lyrics or editing a few fragments. To improve the controllabiliy of the generation process, we further incorporate a keyword prompt to control the lexical choices of the content and propose novel decoding constraints and a vowel modeling task to enable flexible end and internal rhyme schemes. While prior rhyming metrics are mainly for rap lyrics, we propose three novel rhyming evaluation metrics for song lyrics. Both automatic and human evaluations show that the proposed model performs better than the state-of-the-art models in both contents and rhyming quality.