Thailand

Humor is an important social phenomenon, serving complex social and psychological functions. However, despite being studied for millennia humor is computationally not well understood, often considered an AI-complete problem.
In this work, we introduce a novel setting in humor mining: automatically detecting funny and unusual scientific papers. We are inspired by the Ig Nobel prize, a satirical prize awarded annually to celebrate funny scientific achievements (example past winner: “Are cows more likely to lie down the longer they stand?”). This challenging task has unique characteristics that make it particularly suitable for automatic learning.
We construct a dataset containing thousands of funny papers and use it to learn classifiers, combining findings from psychology and linguistics with recent advances in NLP. We use our models to identify potentially funny papers in a large dataset of over 630,000 articles. The results demonstrate the potential of our methods, and more broadly the utility of integrating state-of-the-art NLP methods with insights from more traditional disciplines

ACL-IJCNLP 2021

How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements

computational humor

**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). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

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).

technical paper

This work investigates the use of interactively updated label suggestions to improve upon the efficiency of gathering annotations on the task of opinion mining in German Covid-19 social media data. 
We develop guidelines to conduct a controlled annotation study with social science students and find that suggestions from a model trained on a small, expert-annotated dataset already lead to a substantial improvement – in terms of inter-annotator agreement (+.14 Fleiss’ κ) and annotation quality – compared to students that do not receive any label suggestions. 
We further find that label suggestions from interactively trained models do not lead to an improvement over suggestions from a static model. 
Nonetheless, our analysis of suggestion bias shows that annotators remain capable of reflecting upon the suggested label in general. 
Finally, we confirm the quality of the annotated data in transfer learning experiments between different annotator groups. 
To facilitate further research in opinion mining on social media data, we release our collected data consisting of 200 expert and 2,785 student annotations.

Investigating label suggestions for opinion mining in German Covid-19 social media

We propose an explainable inference approach for science questions by reasoning on grounding and abstract inference chains. This paper frames question answering as a natural language abductive reasoning problem, constructing plausible explanations for each candidate answer and then selecting the candidate with the best explanation as the final answer. Our method, ExplanationLP, elicits explanations by constructing a weighted graph of relevant facts for each candidate answer and employ a linear programming formalism designed to select the optimal subgraph of explanatory facts. The graphs' weighting function is composed of a set of parameters targeting relevance, cohesion and diversity, which we fine-tune for answer selection via Bayesian Optimisation. We carry out our experiments on the WorldTree and ARC-Challenge datasets to empirically demonstrate the following contributions: (1) ExplanationLP obtains strong performance when compared to transformer-based and multi-hop approaches despite having a significantly lower number of parameters (2) We illustrate that our model is able to generate plausible explanations for answer prediction
(3) Our model demonstrates better robustness towards semantic drift when compared to transformer-based and multi-hop approaches

Explainable Inference Over Grounding-Abstract Chains for Science Questions

Modern pre-trained language models are mostly built upon backbones stacking self-attention and feed-forward layers in an interleaved order. In this paper, beyond this stereotyped layer pattern, we aim to improve pre-trained models by exploiting layer variety from two aspects: the layer type set and the layer order. Specifically, besides the original self-attention and feed-forward layers, we introduce convolution into the layer type set, which is experimentally found beneficial to pre-trained models. Furthermore, beyond the original interleaved order, we explore more layer orders to discover more powerful architectures. However, the introduced layer variety leads to a large architecture space of more than billions of candidates, while training a single candidate model from scratch already requires huge computation cost, making it not affordable to search such a space by directly training large amounts of candidate models. To solve this problem, we first pre-train a supernet from which the weights of all candidate models can be inherited, and then adopt an evolutionary algorithm guided by pre-training accuracy to find the optimal architecture. Extensive experiments show that LV-BERT model obtained by our method outperforms BERT and its variants on various downstream tasks. For example, LV-BERT-small achieves 78.8 on the GLUE testing set, 1.8 higher than the strong baseline ELECTRA-small.

LV-BERT: Exploiting Layer Variety for BERT

Report on the journal

This is an incredible moment for NLP. We all routinely work with models whose capabilities would have seemed like science fiction just two decades ago, powerful organizations eagerly await our latest results, and NLP technologies are playing an increasingly large role in shaping our society. As a result, all of us in the NLP community are likely to participate in research that will contribute (to varying degrees and perhaps only indirectly) to technologies that will impact many people’s lives, with both positive and negative consequences for example, technologies that broaden accessibility, enhance creative self-expression, heighten surveillance, and create propaganda. What can we do to fulfill the social responsibility that this brings? As a (very) partial answer to this question, I will review a number of important recent developments, spanning many research groups, concerning dataset creation, model introspection, and system assessment. Taken together, these ideas can help us more reliably characterize how NLP systems will behave, and more reliably communicate this information to a wider range of potential users. In this way, they can help us meet our obligations to the people whose lives are impacted by the results of our research.

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