Thailand

This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence. This extends the well-studied task of fact verification by providing a mechanism to correct written texts that are refuted or only partially supported by evidence. We demonstrate that it is feasible to train factual error correction systems from existing fact checking datasets which only contain labeled claims accompanied by evidence, but not the correction. 
We achieve this by employing a two-stage distant supervision approach that incorporates evidence into masked claims when generating corrections. Our approach, based on the T5 transformer and using retrieved evidence, achieved better results than existing work which used a pointer copy network and gold evidence, producing accurate factual error corrections for 5x more instances in human evaluation and a .125 increase in SARI score. The evaluation is conducted on a dataset of 65,000 instances based on a recent fact verification shared task and we release it to enable further work on the task.

ACL-IJCNLP 2021

Evidence-based Factual Error Correction

fever

sentence modification

evidence

retrieval

natural language generation

fact verification

misinformation

## Welcome to 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics!
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NAACL 2024

2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics

technical paper

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

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Registration is now open

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

This paper studies joint models for selecting correct answer sentences among the top k provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) systems. Our work shows that a critical step to effectively exploiting an answer set regards modeling the interrelated information between pair of answers. For this purpose, we build a three-way multi-classifier, which decides if an answer supports, refutes, or is neutral with respect to another one. More specifically, our neural architecture integrates a state-of-the-art AS2 module with the multi-classifier, and a joint layer connecting all components. We tested our models on WikiQA, TREC-QA, and a real-world dataset. The results show that our models obtain the new state of the art in AS2.

Joint Models for Answer Verification in Question Answering Systems

In open-domain question answering, questions are highly likely to be ambiguous because users may not know the scope of relevant topics when formulating them. 
Therefore, a system needs to find possible interpretations of the question, and predict one or multiple plausible answers.
When multiple plausible answers are found, the system should rewrite the question for each answer to resolve the ambiguity.
In this paper, we present a model that aggregates and combines evidence from multiple passages to adaptively predict a single answer or a set of question-answer pairs for ambiguous questions.
In addition, we propose a novel round-trip prediction approach to iteratively generate additional interpretations that our model fails to find in the first pass, and then verify and filter out the incorrect question-answer pairs to arrive at the final disambiguated output.
Our model, named Refuel, achieves a new state-of-the-art performance on the AmbigQA dataset, and shows competitive performance on NQ-Open and TriviaQA.
The proposed round-trip prediction is a model-agnostic general approach for answering ambiguous open-domain questions, which improves our Refuel as well as several baseline models.
We release source code for our models and experiments at https://github.com/amzn/refuel-open-domain-qa.

Answering Ambiguous Questions through Generative Evidence Fusion and Round-Trip Prediction

Hybrid data combining both tabular and textual content (e.g., financial reports) are quite pervasive in the real world. However, Question Answering (QA) over such hybrid data is largely neglected in existing research. In this work, we extract samples from real financial reports to build a new large-scale QA dataset containing both Tabular And Textual data, named TAT-QA, where numerical reasoning is usually required to infer the answer, such as addition, subtraction, multiplication, division, counting, comparison/sorting, and the compositions. We further propose a novel QA model termed TAGOP, which is capable of reasoning over both tables and text. It adopts sequence tagging to extract relevant cells from the table along with relevant spans from the text to infer their semantics, and then applies symbolic reasoning over them with a set of aggregation operators to arrive at the final answer. TAGOP achieves 58.0% inF1, which is an 11.1% absolute increase over the previous best baseline model, according to our experiments on TAT-QA. But this result still lags far behind performance of expert human, i.e.90.8% in F1. It is demonstrated that our TAT-QA is very challenging and can serve as a benchmark for training and testing powerful QA models that address hybrid form data.

TAT-QA: A Question Answering Benchmark on a Hybrid of Tabular and Textual Content in Finance

Conversational KBQA is about answering a sequence of questions related to a KB. Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history. In this paper, we propose to model these implied entities, which we refer to as the focal entities of the conversation. We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking. Our experiments on two datasets demonstrate the effectiveness of our proposed method.

Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering

Visual Question Answering (VQA) methods aim at leveraging visual input to answer questions that may require complex reasoning over entities. Current models are trained on labelled data that may be insufficient to learn complex knowledge representations. In this paper, we propose a new method to enhance the reasoning capabilities of a multi-modal pretrained model (Vision+Language BERT) by integrating facts extracted from an external knowledge base. Evaluation on the KVQA dataset benchmark demonstrates that our method outperforms competitive baselines by 19%, achieving new state-of-the-art results. We also perform an extensive analysis highlighting the limitations of our best performing model through an ablation study.

In Factuality: Efficient Integration of Relevant Facts for Visual Question Answering

Ever since the advent of deep learning, cross-modal representation learning has been dominated by the approaches involving convolutional neural networks for visual representation and recurrent neural networks for language representation. Transformer architecture, however, has rapidly taken over the recurrent neural networks in natural language processing tasks, and it has also been shown that vision tasks can be handled with transformer architecture, with compatible performance to convolutional neural networks. Such results naturally lead to speculation upon the possibility of tackling cross-modal representation for vision and language exclusively with transformer. This paper examines transformer-exclusive cross-modal representation to explore such possibility, demonstrating its potentials as well as discussing its current limitations and its prospects.

Transformer-Exclusive Cross-Modal Representation for Vision and Language

Neural machine translation suffers when parallel data for training is scarce. Previous works have explored transfer learning to assist training in low-resource scenarios. However, they transfer either from high-resource parallel data, or from monolingual data. In this work, we propose a framework to transfer multiple sources of auxiliary data, including both high-resource parallel data and monolingual data of involved languages. Knowledge in those sources is respectively encoded in a high-resource translation model and pretrained language models, and dually transferred to the low-resource translation model by our approach. Extensive experiments show that our approach yields consistent improvements over strong competitors for multiple translation directions. Furthermore, our approach still exhibits benefit on top of back-translation, making it a useful addition to practitioners' toolbox.

Two Parents, One Child: {D}ual Transfer for Low-Resource Neural Machine Translation

Multilingual text summarization requires the ability to understand documents in multiple languages and generate summaries in the corresponding language, which poses more challenges on current summarization systems. However, this problem has been rarely studied due to the lack of large-scale supervised summarization data in multiple languages. In this paper, we first provide a large-scale multilingual summarization corpus (MLGSum) consisting of 1.1 million articles and summaries in 12 different languages. Based on it, we develop a unified summarization model to understand the document and generate summaries in different languages. We use the contrastive learning strategy to train our multilingual summarization system (CALMS), which consists of two training objectives, contrastive sentence ranking (CSR) and sentence aligned substitution (SAS). The two training objectives are designed to share salient information extractive ability and align sentence-level representation across different languages. Experimental results indicate that CALMS achieves significant improvement over monolingual models in all languages. We further transfer CALMS to other languages and find that it will also benefit similar languages. Our code and dataset will be released in our homepage to encourage research on multilingual text summarization.

Contrastive Aligned Joint Learning for Multilingual Summarization

Discovering whether words are semantically related and identifying the specific semantic relation that holds between them is of crucial importance for automatic reasoning on text data. For that purpose, different methodologies have been proposed that either (1) tackle feature engineering, (2) fine-tune latent semantic spaces, or (3) take advantage of cognitive links between semantic relations in multitask settings. In this paper, we investigate how feature engineering and multitask architectures can be improved and consequently combined to identify lexico-semantic relations. Evaluation results over a set of gold-standard datasets show that (1) combinations of similar features are beneficial (features sets), (2) asymmetric distributional features are a strong cue to discriminate asymmetric relations as well as they play an important role in multitask architectures, (3) shared-private models improve over binary and fully-shared classifiers as well as they correctly balance the focus on features between private and shared layers\footnote{Both the code and the datasets are available at https://anonymous for reproducibility.}.

Understanding Feature Focus in Multitask Settings for Lexico-semantic Relation Identification

Increasing studies leverage pre-trained language models and meta-learning frameworks to solve few-shot text classification problems. Most of the current studies focus on building a meta-learner from the information of input texts but ignore abundant semantic information beneath class labels. In this work, we show that class-label information can be utilized for exacting more discriminative feature representation of the input text from a pre-trained language model like BERT, and can achieve a performance boost when the samples are scarce. Building on top of this discovery, we propose a framework called label-semantic augmented meta-learner (LaSAML) to make full use of label semantics. We systematically investigate various factors in this framework and show that it can be plugged into the existing few-shot text classification system. Through extensive experiments, we demonstrate that the few-shot text classification system upgraded by LaSAML can lead to significant performance improvement over its original counter-parts.

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