Large language models (LLMs) have proficiently solved a broad range of tasks with their rich knowledge but often struggle with logical reasoning. To foster the research on logical reasoning, many benchmarks have been proposed so far. However, most of these benchmarks are limited to English, hindering the evaluation of LLMs specialized for each language. To address this, we propose **JFLD** (**J**apanese **F**ormal **L**ogic **D**eduction), a deductive reasoning benchmark for Japanese. JFLD assess whether LLMs can generate logical steps to (dis-)prove a given hypothesis based on a given set of facts. Its key features are assessing pure logical reasoning abilities isolated from knowledge and assessing various reasoning rules. We evaluate various Japanese LLMs and see that they are still poor at logical reasoning, thus highlighting a substantial need for future research.

This paper investigates the effect of tokenizers on the downstream performance of pretrained language models (PLMs) in scriptio continua languages where no explicit spaces exist between words, using Japanese as a case study. The tokenizer for such languages often consists of a morphological analyzer and a subword tokenizer, requiring us to conduct a comprehensive study of all possible pairs. However, previous studies lack this comprehensiveness. We therefore train extensive sets of tokenizers, build a PLM using each, and measure the downstream performance on a wide range of tasks. Our results demonstrate that each downstream task has a different optimal morphological analyzer, and that it is better to use Byte-Pair-Encoding or Unigram rather than WordPiece as a subword tokenizer, regardless of the type of task.

Masked language modeling (MLM) is a widely used self-supervised pretraining objective, where a model needs to predict an original token that is replaced with a mask given contexts. Although simpler and computationally efficient pretraining objectives, e.g., predicting the first character of a masked token, have recently shown comparable results to MLM, no objectives with a masking scheme actually outperform it in downstream tasks. Motivated by the assumption that their lack of complexity plays a vital role in the degradation, we validate whether more complex masked objectives can achieve better results and investigate how much complexity they should have to perform comparably to MLM. Our results using GLUE, SQuAD, and Universal Dependencies benchmarks demonstrate that more complicated objectives tend to show better downstream results with at least half of the MLM complexity needed to perform comparably to MLM. Finally, we discuss how we should pretrain a model using a masked objective from the task complexity perspective.

One of the challenges in text generation is to control text generation as intended by the user. Previous studies proposed specifying the keywords that should be included in the generated text. However, this approach is insufficient to generate text that reflect the user’s intent. For example, placing an important keyword at the beginning of the text would help attract the reader’s attention; however, existing methods do not enable such flexible control. In this paper, we tackle a novel task of controlling not only keywords but also the position of each keyword in the text generation. To this end, we propose a task-independent method that uses special tokens to control the relative position of keywords. Experimental results on summarization and story generation tasks show that the proposed method can control keywords and their positions. The experimental results also demonstrate that controlling the keyword positions can generate summary texts that are closer to the user’s intent than baseline.

Mining an argument structure from text is an important step for tasks such as argument search and summarization. While studies on argument(ation) mining have proposed promising neural network models, they usually suffer from a shortage of training data. To address this issue, we expand the training data with various auxiliary argument mining corpora and propose an end-to-end cross-corpus training method called Multi-Task Argument Mining (MT-AM). To evaluate our approach, we conducted experiments for the main argument mining tasks on several well-established argument mining corpora. The results demonstrate that MT-AM generally outperformed the models trained on a single corpus. Also, the smaller the target corpus was, the better the MT-AM performed. Our extensive analyses suggest that the improvement of MT-AM depends on several factors of transferability among auxiliary and target corpora.

This paper describes the first report on cross-lingual transfer for semantic dependency parsing. We present the insight that there are twodifferent kinds of cross-linguality, namely sur-face level and mantic level, and try to cap-ture both kinds of cross-linguality by combin-ing annotation projection and model transferof pre-trained language models. Our exper-iments showed that the performance of our graph-based semantic dependency parser almost achieved the approximated upper bound.

State-of-the-art argument mining studies have advanced the techniques for predicting argument structures. However, the technology for capturing non-tree-structured arguments is still in its infancy. In this paper, we focus on non-tree argument mining with a neural model. We jointly predict proposition types and edges between propositions. Our proposed model incorporates (i) task-specific parameterization (TSP) that effectively encodes a sequence of propositions and (ii) a proposition-level biaffine attention (PLBA) that can predict a non-tree argument consisting of edges. Experimental results show that both TSP and PLBA boost edge prediction performance compared to baselines.

This paper presents our proposed parser for the shared task on Meaning Representation Parsing (MRP 2020) at CoNLL, where participant systems were required to parse five types of graphs in different languages. We propose to unify these tasks as a text-to-graph-notation transduction in which we convert an input text into a graph notation. To this end, we designed a novel Plain Graph Notation (PGN) that handles various graphs universally. Then, our parser predicts a PGN-based sequence by leveraging Transformers and biaffine attentions. Notably, our parser can handle any PGN-formatted graphs with fewer framework-specific modifications. As a result, ensemble versions of the parser tied for 1st place in both cross-framework and cross-lingual tracks.

In this paper, we present our system for SemEval-2020 task 3, Predicting the (Graded) Effect of Context in Word Similarity. Due to the unsupervised nature of the task, we concentrated on inquiring about the similarity measures induced by different layers of different pre-trained Transformer-based language models, which can be good approximations of the human sense of word similarity. Interestingly, our experiments reveal a language-independent characteristic: the middle to upper layers of Transformer-based language models can induce good approximate similarity measures. Finally, our system was ranked 1st on the Slovenian part of Subtask1 and 2nd on the Croatian part of both Subtask1 and Subtask2.

This paper describes the winning system for SemEval-2020 task 7: Assessing Humor in Edited News Headlines. Our strategy is Stacking at Scale (SaS) with heterogeneous pre-trained language models (PLMs) such as BERT and GPT-2. SaS first performs fine-tuning on numbers of PLMs with various hyperparameters and then applies a powerful stacking ensemble on top of the fine-tuned PLMs. Our experimental results show that SaS outperforms a naive average ensemble, leveraging weaker PLMs as well as high-performing PLMs. Interestingly, the results show that SaS captured non-funny semantics. Consequently, the system was ranked 1st in all subtasks by significant margins compared with other systems.

Users of social networking services often share their emotions via multi-modal content, usually images paired with text embedded in them. SemEval-2020 task 8, Memotion Analysis, aims at automatically recognizing these emotions of so-called internet memes. In this paper, we propose a simple but effective Modality Ensemble that incorporates visual and textual deep-learning models, which are independently trained, rather than providing a single multi-modal joint network. To this end, we first fine-tune four pre-trained visual models (i.e., Inception-ResNet, PolyNet, SENet, and PNASNet) and four textual models (i.e., BERT, GPT-2, Transformer-XL, and XLNet). Then, we fuse their predictions with ensemble methods to effectively capture cross-modal correlations. The experiments performed on dev-set show that both visual and textual features aided each other, especially in subtask-C, and consequently, our system ranked 2nd on subtask-C.

This paper shows our system for SemEval-2020 task 10, Emphasis Selection for Written Text in Visual Media. Our strategy is two-fold. First, we propose fine-tuning many pre-trained language models, predicting an emphasis probability distribution over tokens. Then, we propose stacking a trainable distribution fusion DistFuse system to fuse the predictions of the fine-tuned models. Experimental results show tha DistFuse is comparable or better when compared with a naive average ensemble. As a result, we were ranked 2nd amongst 31 teams.

In this paper, we show our system for SemEval-2020 task 11, where we tackle propaganda span identification (SI) and technique classification (TC). We investigate heterogeneous pre-trained language models (PLMs) such as BERT, GPT-2, XLNet, XLM, RoBERTa, and XLM-RoBERTa for SI and TC fine-tuning, respectively. In large-scale experiments, we found that each of the language models has a characteristic property, and using an ensemble model with them is promising. Finally, the ensemble model was ranked 1st amongst 35 teams for SI and 3rd amongst 31 teams for TC.

This paper describes the proposed system of the Hitachi team for the Cross-Framework Meaning Representation Parsing (MRP 2019) shared task. In this shared task, the participating systems were asked to predict nodes, edges and their attributes for five frameworks, each with different order of “abstraction” from input tokens. We proposed a unified encoder-to-biaffine network for all five frameworks, which effectively incorporates a shared encoder to extract rich input features, decoder networks to generate anchorless nodes in UCCA and AMR, and biaffine networks to predict edges. Our system was ranked fifth with the macro-averaged MRP F1 score of 0.7604, and outperformed the baseline unified transition-based MRP. Furthermore, post-evaluation experiments showed that we can boost the performance of the proposed system by incorporating multi-task learning, whereas the baseline could not. These imply efficacy of incorporating the biaffine network to the shared architecture for MRP and that learning heterogeneous meaning representations at once can boost the system performance.