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In this paper we implement and compare 7 different data augmentation strategies for the task of automatic scoring of children&#39;s ability to understand others&#39; thoughts, feelings, and desires (or ``mindreading&#39;&#39;). 

We recruit in-domain experts to re-annotate augmented samples and determine to what extent each strategy preserves the original rating. We also carry out multiple experiments to measure how much each augmentation strategy improves the performance of automatic scoring systems. To determine the capabilities of automatic systems to generalize to unseen data, we create UK-MIND-20 - a new corpus of children&#39;s performance on tests of mindreading, consisting of 10,320 question-answer pairs.

We obtain a new state-of-the-art performance on the MIND-CA corpus, improving macro-F1-score by 6 points. Results indicate that both the number of training examples and the quality of the augmentation strategies affect the performance of the systems. The task-specific augmentations generally outperform task-agnostic augmentations. Automatic augmentations based on vectors (GloVe, FastText) perform the worst. 

We find that systems trained on MIND-CA generalize well to UK-MIND-20. We demonstrate that data augmentation strategies also improve the performance on unseen data.

IJCNLP-AACL 2021

Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability

mindreading

theory of mind

automatic scoring

psycholinguistics

data augmentation

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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).

In this paper we implement and compare 7 different data augmentation strategies for the task of automatic scoring of children's ability to understand others' thoughts, feelings, and desires (or ``mindreading''). 

We recruit in-domain experts to re-annotate augmented samples and determine to what extent each strategy preserves the original rating. We also carry out multiple experiments to measure how much each augmentation strategy improves the performance of automatic scoring systems. To determine the capabilities of automatic systems to generalize to unseen data, we create UK-MIND-20 - a new corpus of children's performance on tests of mindreading, consisting of 10,320 question-answer pairs.

We obtain a new state-of-the-art performance on the MIND-CA corpus, improving macro-F1-score by 6 points. Results indicate that both the number of training examples and the quality of the augmentation strategies affect the performance of the systems. The task-specific augmentations generally outperform task-agnostic augmentations. Automatic augmentations based on vectors (GloVe, FastText) perform the worst. 

We find that systems trained on MIND-CA generalize well to UK-MIND-20. We demonstrate that data augmentation strategies also improve the performance on unseen data.

poster

A growing body of literature has focused on detailing the linguistic knowledge embedded in large, pretrained language models. Existing work has shown that non-linguistic biases in models can drive model behavior away from linguistic generalizations. We hypothesized that competing linguistic processes within a language, rather than just non-linguistic model biases, could obscure underlying linguistic knowledge. We tested this claim by exploring a single phenomenon in four languages: English, Chinese, Spanish, and Italian. While human behavior has been found to be similar across languages, we find cross-linguistic variation in model behavior. We show that competing processes in a language act as constraints on model behavior and demonstrate that targeted fine-tuning can re-weight the learned constraints, uncovering otherwise dormant linguistic knowledge in models. Our results suggest that models need to learn both the linguistic constraints in a language and their relative ranking, with mismatches in either producing non-human-like behavior.

Uncovering Constraint-Based Behavior in Neural Models via Targeted Fine-Tuning

Interpretability is an important aspect of the trustworthiness of a model's predictions. Transformer's predictions are widely explained by the attention weights, i.e., a probability distribution generated at its self-attention unit (head). Current empirical studies provide shreds of evidence that attention weights are not explanations by proving that they are not unique. A recent study showed theoretical justifications to this observation by proving the non-identifiability of attention weights. For a given input to a head and its output, if the attention weights generated in it are unique, we call the weights identifiable. In this work, we provide deeper theoretical analysis and empirical observations on the identifiability of attention weights. Ignored in the previous works, we find the attention weights are more identifiable than we currently perceive by uncovering the hidden role of the key vector. However, the weights are still prone to be non-unique attentions that make them unfit for interpretation. To tackle this issue, we provide a variant of the encoder layer that decouples the relationship between key and value vector and provides identifiable weights up to the desired length of the input. We prove the applicability of such variations by providing empirical justifications on varied text classification tasks. The implementations are available at https://github.com/declare-lab/identifiable-transformers.

More Identifiable yet Equally Performant Transformers for Text Classification

Natural Language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language.
For large-scale conversational systems, where it is common to have over hundreds of intents and thousands of slots, neither template-based approaches nor model-based approaches are scalable.
Recently, neural NLGs started leveraging transfer learning and showed promising results in few-shot settings.
This paper proposes AugNLG, a novel data augmentation approach that combines a self-trained neural retrieval model with a few-shot learned NLU model, to automatically create MR-to-Text data from open-domain texts.
The proposed system mostly outperforms the state-of-the-art methods on the FewshotWOZ data in both BLEU and Slot Error Rate. 
We further confirm improved results on the FewshotSGD data and provide comprehensive analysis results on key components of our system. Our code and data are available at https://github.com/XinnuoXu/AugNLG.

AugNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation

Sequential sentence classification aims to classify each sentence in the document based on the context in which sentences appear. Most existing work addresses this problem using a hierarchical sequence labeling network. However, they ignore considering the latent segment structure of the document, in which contiguous sentences often have coherent semantics. In this paper, we proposed a span-based dynamic local attention model that could explicitly capture the structural information by the proposed supervised dynamic local attention. We further introduce an auxiliary task called span-based classification to explore the span-level representations. Extensive experiments show that our model achieves better or competitive performance against state-of-the-art baselines on two benchmark datasets.

A Span-based Dynamic Local Attention Model for Sequential Sentence Classification

Ordered word sequences contain the rich structures that define language. However, it's often not clear if or how modern pretrained language models utilize these structures. We show that the token representations and self-attention activations within BERT are surprisingly resilient to shuffling the order of input tokens, and that for several GLUE language understanding tasks, shuffling only minimally degrades performance, e.g., by 4% for QNLI. While bleak from the perspective of language understanding, our results have positive implications for cases where copyright or ethics necessitates the consideration of bag-of-words data (vs. full documents). We simulate such a scenario for three sensitive classification tasks, demonstrating minimal performance degradation vs. releasing full language sequences.

How effective is BERT without word ordering? Implications for language understanding and data privacy

Individuals with autism spectrum disorder (ASD) experience difficulties in social aspects of communication, but the linguistic characteristics associated with deficits in discourse and pragmatic expression are often difficult to precisely identify and quantify. We are currently collecting a corpus of transcribed natural conversations produced in an experimental setting in which participants with and without ASD complete a number of collaborative tasks with their neurotypical peers. Using this dyadic conversational data, we investigate three pragmatic features – politeness, uncertainty, and informativeness – and present a dataset of utterances annotated for each of these features on a three-point scale. We then introduce ongoing work in developing and training neural models to automatically predict these features, with the goal of identifying the same between-groups differences that are observed using manual annotations. We find the best performing model for all three features is a feedforward neural network trained with BERT embeddings. Our models yield higher acc racy than ones used in previous approaches for deriving these features, with F1 exceeding 0.82 for all three pragmatic features.

Predicting pragmatic discourse features in the language of adults with autism spectrum disorder

We present the first Chinese adversarial natural language inference (NLI) evaluation set (CANLI) to probe the limits of pretrained language models (PLMs). CANLI is based on the linguistic phenomenon of causative- passive homonymy (CPH). The disambiguation of CPH is easy for humans, but difficult for machines, as the causative and the passive are not distinguished by the sentences’ syntactic structure. We created the CANLI in two related settings: (i) CA: a syntactic heuristic setting and (ii) CB: a semantic role labeling setting. We find that state-of-the-art PLMs that are fine-tuned on existing large-scale Chinese NLI benchmark datasets perform poorly on CANLI. Further, we notice that the PLM’s performance on CA and CB is independent of each other, suggesting that the PLM has not learned the shared underlying linguistic knowledge CPH, but instead the respective spurious patterns. We conclude that there is considerable room for improvement in the NLI system.

Adversarial Datasets for NLI Tasks: the Case of the Chinese Causative-Passive Homonymy

**Read paper:**
https://aclanthology.org/2021.acl-srw.30/

Most earlier work on text summarization is carried out on news article datasets. The summary in these datasets is naturally located at the beginning of the text. Hence, a model can spuriously utilize this correlation for summary gen- eration instead of truly learning to summarize. To address this issue, we constructed a new dataset, SUMPUBMED, using scientific articles from the PubMed archive. We conducted a human analysis of summary coverage, redundancy, readability, coherence, and informativeness on SUMPUBMED. SUMPUBMED is challenging because (a) the summary is distributed throughout the text (not-localized on top), and (b) it contains rare domain-specific scientific terms. We observe that seq2seq models that adequately summarize news articles struggle to summarize SUMPUBMED. Thus, SUMPUBMED opens new avenues for the future improvement of models as well as the development of new evaluation metrics.

SUMPUBMED: Summarization Dataset of PubMed Scientific Articles

Human-like syntactic generalization abilities of neural language models (LMs) have received wide attention, but discourse-level knowledge in these models has been less explored. In this study, we analyze the LMs with respect to a particular aspect of discourse, topic-comment structure, a crucial part of text production. To separately analyze the knowledge of LMs on topic-comment structure, we chose the Japanese language, a topic-prominent language, for designing probing tasks. Experimental results suggest that LMs have different generalizations from humans in terms of the topic-comment structure. These results imply that LMs do not perform human-like discourse processing in text generation.

Topicalization in Language Models: A Case Study on Japanese

The events that took place at the Unite the Right rally held in Charlottesville, Virginia on August 11-12, 2017 caused intense reaction on social media from users across the political spectrum. We present a novel application of psycholinguistics - specifically, construal level theory - to analyze the language on social media around this event of social import through topic models. We find that including psycholinguistic measures of concreteness as covariates in topic models can lead to informed analysis of the language surrounding an event of political import.

Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children’s mindreading ability

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