yuz9yuz/NLPImpact huggingface dataset:
Title: Leveraging Unimodal Self-Supervised Learning for Multimodal Audio-Visual Speech Recognition
Venue: ACL (2022)
Topic: multimodality and language grounding to vision, robotics and beyond
Abstract: Training Transformer-based models demands a large amount of data, while obtaining aligned and labelled data in multimodality is rather cost-demanding, especially for audio-visual speech recognition (AVSR). Thus it makes a lot of sense to make use of unlabelled unimodal data. On the other side, although the effectiveness of large-scale self-supervised learning is well established in both audio and visual modalities, how to integrate those pre-trained models into a multimodal scenario remains underexplored. In this work, we successfully leverage unimodal self-supervised learning to promote the multimodal AVSR. In particular, audio and visual front-ends are trained on large-scale unimodal datasets, then we integrate components of both front-ends into a larger multimodal framework which learns to recognize parallel audio-visual data into characters through a combination of CTC and seq2seq decoding. We show that both components inherited from unimodal self-supervised learning cooperate well, resulting in that the multimodal framework yields competitive results through fine-tuning. Our model is experimentally validated on both word-level and sentence-level tasks. Especially, even without an external language model, our proposed model raises the state-of-the-art performances on the widely accepted Lip Reading Sentences 2 (LRS2) dataset by a large margin, with a relative improvement of 30%.
Title: SummaReranker: A Multi-Task Mixture-of-Experts Re-ranking Framework for Abstractive Summarization
Venue: ACL (2022)
Topic: summarization
Abstract: Sequence-to-sequence neural networks have recently achieved great success in abstractive summarization, especially through fine-tuning large pre-trained language models on the downstream dataset. These models are typically decoded with beam search to generate a unique summary. However, the search space is very large, and with the exposure bias, such decoding is not optimal. In this paper, we show that it is possible to directly train a second-stage model performing re-ranking on a set of summary candidates. Our mixture-of-experts SummaReranker learns to select a better candidate and consistently improves the performance of the base model. With a base PEGASUS, we push ROUGE scores by 5.44% on CNN- DailyMail (47.16 ROUGE-1), 1.31% on XSum (48.12 ROUGE-1) and 9.34% on Reddit TIFU (29.83 ROUGE-1), reaching a new state-of-the-art. Our code and checkpoints will be available at https://github.com/ntunlp/SummaReranker.
Title: Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals
Venue: ACL (2022)
Topic: multimodality and language grounding to vision, robotics and beyond
Abstract: The ability to sequence unordered events is evidence of comprehension and reasoning about real world tasks/procedures. It is essential for applications such as task planning and multi-source instruction summarization. It often requires thorough understanding of temporal common sense and multimodal information, since these procedures are often conveyed by a combination of texts and images. While humans are capable of reasoning about and sequencing unordered procedural instructions, the extent to which the current machine learning methods possess such capability is still an open question. In this work, we benchmark models’ capability of reasoning over and sequencing unordered multimodal instructions by curating datasets from online instructional manuals and collecting comprehensive human annotations. We find current state-of-the-art models not only perform significantly worse than humans but also seem incapable of efficiently utilizing multimodal information. To improve machines’ performance on multimodal event sequencing, we propose sequence-aware pretraining techniques exploiting the sequential alignment properties of both texts and images, resulting in > 5% improvements on perfect match ratio.
Title: Zoom Out and Observe: News Environment Perception for Fake News Detection
Venue: ACL (2022)
Topic: computational social science and cultural analytics
Abstract: Fake news detection is crucial for preventing the dissemination of misinformation on social media. To differentiate fake news from real ones, existing methods observe the language patterns of the news post and “zoom in” to verify its content with knowledge sources or check its readers’ replies. However, these methods neglect the information in the external news environment where a fake news post is created and disseminated. The news environment represents recent mainstream media opinion and public attention, which is an important inspiration of fake news fabrication because fake news is often designed to ride the wave of popular events and catch public attention with unexpected novel content for greater exposure and spread. To capture the environmental signals of news posts, we “zoom out” to observe the news environment and propose the News Environment Perception Framework (NEP). For each post, we construct its macro and micro news environment from recent mainstream news. Then we design a popularity-oriented and a novelty-oriented module to perceive useful signals and further assist final prediction. Experiments on our newly built datasets show that the NEP can efficiently improve the performance of basic fake news detectors.
Title: Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models
Venue: ACL (2022)
Topic: machine translation
Abstract: Multi-encoder models are a broad family of context-aware neural machine translation systems that aim to improve translation quality by encoding document-level contextual information alongside the current sentence. The context encoding is undertaken by contextual parameters, trained on document-level data. In this work, we discuss the difficulty of training these parameters effectively, due to the sparsity of the words in need of context (i.e., the training signal), and their relevant context. We propose to pre-train the contextual parameters over split sentence pairs, which makes an efficient use of the available data for two reasons. Firstly, it increases the contextual training signal by breaking intra-sentential syntactic relations, and thus pushing the model to search the context for disambiguating clues more frequently. Secondly, it eases the retrieval of relevant context, since context segments become shorter. We propose four different splitting methods, and evaluate our approach with BLEU and contrastive test sets. Results show that it consistently improves learning of contextual parameters, both in low and high resource settings.
Title: Saliency as Evidence: Event Detection with Trigger Saliency Attribution
Venue: ACL (2022)
Topic: information extraction
Abstract: Event detection (ED) is a critical subtask of event extraction that seeks to identify event triggers of certain types in texts. Despite significant advances in ED, existing methods typically follow a “one model fits all types” approach, which sees no differences between event types and often results in a quite skewed performance. Finding the causes of skewed performance is crucial for the robustness of an ED model, but to date there has been little exploration of this problem. This research examines the issue in depth and presents a new concept termed trigger salience attribution, which can explicitly quantify the underlying patterns of events. On this foundation, we develop a new training mechanism for ED, which can distinguish between trigger-dependent and context-dependent types and achieve promising performance on two benchmarks. Finally, by highlighting many distinct characteristics of trigger-dependent and context-dependent types, our work may promote more research into this problem.
Title: SRL4E – Semantic Role Labeling for Emotions: A Unified Evaluation Framework
Venue: ACL (2022)
Topic: sentiment analysis, stylistic analysis, and argument mining
Abstract: In the field of sentiment analysis, several studies have highlighted that a single sentence may express multiple, sometimes contrasting, sentiments and emotions, each with its own experiencer, target and/or cause. To this end, over the past few years researchers have started to collect and annotate data manually, in order to investigate the capabilities of automatic systems not only to distinguish between emotions, but also to capture their semantic constituents. However, currently available gold datasets are heterogeneous in size, domain, format, splits, emotion categories and role labels, making comparisons across different works difficult and hampering progress in the area. In this paper, we tackle this issue and present a unified evaluation framework focused on Semantic Role Labeling for Emotions (SRL4E), in which we unify several datasets tagged with emotions and semantic roles by using a common labeling scheme. We use SRL4E as a benchmark to evaluate how modern pretrained language models perform and analyze where we currently stand in this task, hoping to provide the tools to facilitate studies in this complex area.
Title: Context Matters: A Pragmatic Study of PLMs’ Negation Understanding
Venue: ACL (2022)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Abstract: In linguistics, there are two main perspectives on negation: a semantic and a pragmatic view. So far, research in NLP on negation has almost exclusively adhered to the semantic view. In this article, we adopt the pragmatic paradigm to conduct a study of negation understanding focusing on transformer-based PLMs. Our results differ from previous, semantics-based studies and therefore help to contribute a more comprehensive – and, given the results, much more optimistic – picture of the PLMs’ negation understanding.
Title: Probing for Predicate Argument Structures in Pretrained Language Models
Venue: ACL (2022)
Topic: linguistic theories, cognitive modeling and psycholinguistics
Abstract: Thanks to the effectiveness and wide availability of modern pretrained language models (PLMs), recently proposed approaches have achieved remarkable results in dependency- and span-based, multilingual and cross-lingual Semantic Role Labeling (SRL). These results have prompted researchers to investigate the inner workings of modern PLMs with the aim of understanding how, where, and to what extent they encode information about SRL. In this paper, we follow this line of research and probe for predicate argument structures in PLMs. Our study shows that PLMs do encode semantic structures directly into the contextualized representation of a predicate, and also provides insights into the correlation between predicate senses and their structures, the degree of transferability between nominal and verbal structures, and how such structures are encoded across languages. Finally, we look at the practical implications of such insights and demonstrate the benefits of embedding predicate argument structure information into an SRL model.
Title: Multilingual Generative Language Models for Zero-Shot Cross-Lingual Event Argument Extraction
Venue: ACL (2022)
Topic: information extraction
Abstract: We present a study on leveraging multilingual pre-trained generative language models for zero-shot cross-lingual event argument extraction (EAE). By formulating EAE as a language generation task, our method effectively encodes event structures and captures the dependencies between arguments. We design language-agnostic templates to represent the event argument structures, which are compatible with any language, hence facilitating the cross-lingual transfer. Our proposed model finetunes multilingual pre-trained generative language models to generate sentences that fill in the language-agnostic template with arguments extracted from the input passage. The model is trained on source languages and is then directly applied to target languages for event argument extraction. Experiments demonstrate that the proposed model outperforms the current state-of-the-art models on zero-shot cross-lingual EAE. Comprehensive studies and error analyses are presented to better understand the advantages and the current limitations of using generative language models for zero-shot cross-lingual transfer EAE.
Title: Identifying Moments of Change from Longitudinal User Text
Venue: ACL (2022)
Topic: human-centered nlp
Abstract: Identifying changes in individuals’ behaviour and mood, as observed via content shared on online platforms, is increasingly gaining importance. Most research to-date on this topic focuses on either: (a) identifying individuals at risk or with a certain mental health condition given a batch of posts or (b) providing equivalent labels at the post level. A disadvantage of such work is the lack of a strong temporal component and the inability to make longitudinal assessments following an individual’s trajectory and allowing timely interventions. Here we define a new task, that of identifying moments of change in individuals on the basis of their shared content online. The changes we consider are sudden shifts in mood (switches) or gradual mood progression (escalations). We have created detailed guidelines for capturing moments of change and a corpus of 500 manually annotated user timelines (18.7K posts). We have developed a variety of baseline models drawing inspiration from related tasks and show that the best performance is obtained through context aware sequential modelling. We also introduce new metrics for capturing rare events in temporal windows.
Title: Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System
Venue: ACL (2022)
Topic: dialogue and interactive systems
Abstract: Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. Despite their success, existing methods often formulate this task as a cascaded generation problem which can lead to error accumulation across different sub-tasks and greater data annotation overhead. In this study, we present PPTOD, a unified plug-and-play model for task-oriented dialogue. In addition, we introduce a new dialogue multi-task pre-training strategy that allows the model to learn the primary TOD task completion skills from heterogeneous dialog corpora. We extensively test our model on three benchmark TOD tasks, including end-to-end dialogue modelling, dialogue state tracking, and intent classification. Experimental results show that PPTOD achieves new state of the art on all evaluated tasks in both high-resource and low-resource scenarios. Furthermore, comparisons against previous SOTA methods show that the responses generated by PPTOD are more factually correct and semantically coherent as judged by human annotators.
Title: Graph Enhanced Contrastive Learning for Radiology Findings Summarization
Venue: ACL (2022)
Topic: summarization
Abstract: The impression section of a radiology report summarizes the most prominent observation from the findings section and is the most important section for radiologists to communicate to physicians. Summarizing findings is time-consuming and can be prone to error for inexperienced radiologists, and thus automatic impression generation has attracted substantial attention. With the encoder-decoder framework, most previous studies explore incorporating extra knowledge (e.g., static pre-defined clinical ontologies or extra background information). Yet, they encode such knowledge by a separate encoder to treat it as an extra input to their models, which is limited in leveraging their relations with the original findings. To address the limitation, we propose a unified framework for exploiting both extra knowledge and the original findings in an integrated way so that the critical information (i.e., key words and their relations) can be extracted in an appropriate way to facilitate impression generation. In detail, for each input findings, it is encoded by a text encoder and a graph is constructed through its entities and dependency tree. Then, a graph encoder (e.g., graph neural networks (GNNs)) is adopted to model relation information in the constructed graph. Finally, to emphasize the key words in the findings, contrastive learning is introduced to map positive samples (constructed by masking non-key words) closer and push apart negative ones (constructed by masking key words). The experimental results on two datasets, OpenI and MIMIC-CXR, confirm the effectiveness of our proposed method, where the state-of-the-art results are achieved.
Title: Semi-Supervised Formality Style Transfer with Consistency Training
Venue: ACL (2022)
Topic: sentiment analysis, stylistic analysis, and argument mining
Abstract: Formality style transfer (FST) is a task that involves paraphrasing an informal sentence into a formal one without altering its meaning. To address the data-scarcity problem of existing parallel datasets, previous studies tend to adopt a cycle-reconstruction scheme to utilize additional unlabeled data, where the FST model mainly benefits from target-side unlabeled sentences. In this work, we propose a simple yet effective semi-supervised framework to better utilize source-side unlabeled sentences based on consistency training. Specifically, our approach augments pseudo-parallel data obtained from a source-side informal sentence by enforcing the model to generate similar outputs for its perturbed version. Moreover, we empirically examined the effects of various data perturbation methods and propose effective data filtering strategies to improve our framework. Experimental results on the GYAFC benchmark demonstrate that our approach can achieve state-of-the-art results, even with less than 40% of the parallel data.
Title: Cross-Lingual Ability of Multilingual Masked Language Models: A Study of Language Structure
Venue: ACL (2022)
Topic: multilinguality and language diversity
Abstract: Multilingual pre-trained language models, such as mBERT and XLM-R, have shown impressive cross-lingual ability. Surprisingly, both of them use multilingual masked language model (MLM) without any cross-lingual supervision or aligned data. Despite the encouraging results, we still lack a clear understanding of why cross-lingual ability could emerge from multilingual MLM. In our work, we argue that cross-language ability comes from the commonality between languages. Specifically, we study three language properties: constituent order, composition and word co-occurrence. First, we create an artificial language by modifying property in source language. Then we study the contribution of modified property through the change of cross-language transfer results on target language. We conduct experiments on six languages and two cross-lingual NLP tasks (textual entailment, sentence retrieval). Our main conclusion is that the contribution of constituent order and word co-occurrence is limited, while the composition is more crucial to the success of cross-linguistic transfer.
Title: Rare and Zero-shot Word Sense Disambiguation using Z-Reweighting
Venue: ACL (2022)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Abstract: Word sense disambiguation (WSD) is a crucial problem in the natural language processing (NLP) community. Current methods achieve decent performance by utilizing supervised learning and large pre-trained language models. However, the imbalanced training dataset leads to poor performance on rare senses and zero-shot senses. There are more training instances and senses for words with top frequency ranks than those with low frequency ranks in the training dataset. We investigate the statistical relation between word frequency rank and word sense number distribution. Based on the relation, we propose a Z-reweighting method on the word level to adjust the training on the imbalanced dataset. The experiments show that the Z-reweighting strategy achieves performance gain on the standard English all words WSD benchmark. Moreover, the strategy can help models generalize better on rare and zero-shot senses.
Title: Nibbling at the Hard Core of Word Sense Disambiguation
Venue: ACL (2022)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Abstract: With state-of-the-art systems having finally attained estimated human performance, Word Sense Disambiguation (WSD) has now joined the array of Natural Language Processing tasks that have seemingly been solved, thanks to the vast amounts of knowledge encoded into Transformer-based pre-trained language models. And yet, if we look below the surface of raw figures, it is easy to realize that current approaches still make trivial mistakes that a human would never make. In this work, we provide evidence showing why the F1 score metric should not simply be taken at face value and present an exhaustive analysis of the errors that seven of the most representative state-of-the-art systems for English all-words WSD make on traditional evaluation benchmarks. In addition, we produce and release a collection of test sets featuring (a) an amended version of the standard evaluation benchmark that fixes its lexical and semantic inaccuracies, (b) 42D, a challenge set devised to assess the resilience of systems with respect to least frequent word senses and senses not seen at training time, and (c) hardEN, a challenge set made up solely of instances which none of the investigated state-of-the-art systems can solve. We make all of the test sets and model predictions available to the research community at https://github.com/SapienzaNLP/wsd-hard-benchmark.
Title: Large Scale Substitution-based Word Sense Induction
Venue: ACL (2022)
Topic: semantics: lexical, sentence-level semantics, textual inference and other areas
Abstract: We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is sense-tagged according to a corpus-derived sense inventory and where each sense is associated with indicative words. Evaluation on English Wikipedia that was sense-tagged using our method shows that both the induced senses, and the per-instance sense assignment, are of high quality even compared to WSD methods, such as Babelfy. Furthermore, by training a static word embeddings algorithm on the sense-tagged corpus, we obtain high-quality static senseful embeddings. These outperform existing senseful embeddings methods on the WiC dataset and on a new outlier detection dataset we developed. The data driven nature of the algorithm allows to induce corpora-specific senses, which may not appear in standard sense inventories, as we demonstrate using a case study on the scientific domain.
Title: Can Synthetic Translations Improve Bitext Quality?
Venue: ACL (2022)
Topic: nlp applications
Abstract: Synthetic translations have been used for a wide range of NLP tasks primarily as a means of data augmentation. This work explores, instead, how synthetic translations can be used to revise potentially imperfect reference translations in mined bitext. We find that synthetic samples can improve bitext quality without any additional bilingual supervision when they replace the originals based on a semantic equivalence classifier that helps mitigate NMT noise. The improved quality of the revised bitext is confirmed intrinsically via human evaluation and extrinsically through bilingual induction and MT tasks.