Thailand

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.}.

IJCNLP-AACL 2021

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

lexico-sematnic relation

feature engineering

multitask

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

technical paper

This paper presents the first large-scale meta-evaluation of machine translation (MT). We annotated MT evaluations conducted in 769 research papers published from 2010 to 2020. Our study shows that practices for automatic MT evaluation have dramatically changed during the past decade and follow concerning trends. An increasing number of MT evaluations exclusively rely on differences between BLEU scores to draw conclusions, without performing any kind of statistical significance testing nor human evaluation, while at least 108 metrics claiming to be better than BLEU have been proposed. MT evaluations in recent papers tend to copy and compare automatic metric scores from previous work to claim the superiority of a method or an algorithm without confirming neither exactly the same training, validating, and testing data have been used nor the metric scores are comparable. Furthermore, tools for reporting standardized metric scores are still far from being widely adopted by the MT community. After showing how the accumulation of these pitfalls leads to dubious evaluation, we propose a guideline to encourage better automatic MT evaluation along with a simple meta-evaluation scoring method to assess its credibility.

Scientific Credibility of Machine Translation Research: A Meta-Evaluation of 769 Papers

SOTA coreference resolution produces increasingly impressive scores on the OntoNotes benchmark. However lack of comparable data following the same scheme for more genres makes it difficult to evaluate generalizability to open domain data. This paper provides a dataset and comprehensive evaluation showing that the latest neural LM based end-to-end systems degrade very substantially out of domain. We make an OntoNotes-like coreference dataset called OntoGUM publicly available, converted from GUM, an English corpus covering 12 genres, using deterministic rules, which we evaluate. Thanks to the rich syntactic and discourse annotations in GUM, we are able to create the largest human-annotated coreference corpus following the OntoNotes guidelines, and the first to be evaluated for consistency with the OntoNotes scheme. Out-of-domain evaluation across 12 genres shows nearly 15-20% degradation for both deterministic and deep learning systems, indicating a lack of generalizability or covert overfitting in existing coreference resolution models.

OntoGUM: Evaluating Contextualized SOTA Coreference Resolution on 12 More Genres

Recent work has investigated the interesting question using pre-trained language models (PLMs) as knowledge bases for answering open questions. However, existing work is limited in using small benchmarks with high test-train overlaps. We construct a new dataset of closed-book QA using SQuAD, and investigate the performance of BART. Experiments show that it is challenging for BART to remember training facts in high precision, and also challenging to answer closed-book questions even if relevant knowledge is retained. Some promising directions are found, including decoupling the knowledge memorizing process and the QA finetune process, forcing the model to recall relevant knowledge when question answering.

Can Generative Pre-trained Language Models Serve As Knowledge Bases for Closed-book QA?

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

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.

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

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

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