Among the most critical limitations of deep learning NLP models are their lack of interpretability, and their reliance on spurious correlations. Prior work proposed various approaches to interpreting the black-box models to unveil the spurious correl
ations, but the research was primarily used in human-computer interaction scenarios. It still remains underexplored whether or how such model interpretations can be used to automatically unlearn'' confounding features. In this work, we propose influence tuning---a procedure that leverages model interpretations to update the model parameters towards a plausible interpretation (rather than an interpretation that relies on spurious patterns in the data) in addition to learning to predict the task labels. We show that in a controlled setup, influence tuning can help deconfounding the model from spurious patterns in data, significantly outperforming baseline methods that use adversarial training.
Recent research has documented that results reported in frequently-cited authorship attribution papers are difficult to reproduce. Inaccessible code and data are often proposed as factors which block successful reproductions. Even when original mater
ials are available, problems remain which prevent researchers from comparing the effectiveness of different methods. To solve the remaining problems---the lack of fixed test sets and the use of inappropriately homogeneous corpora---our paper contributes materials for five closed-set authorship identification experiments. The five experiments feature texts from 106 distinct authors. Experiments involve a range of contemporary non-fiction American English prose. These experiments provide the foundation for comparable and reproducible authorship attribution research involving contemporary writing.
Cross-language authorship attribution is the challenging task of classifying documents by bilingual authors where the training documents are written in a different language than the evaluation documents. Traditional solutions rely on either translati
on to enable the use of single-language features, or language-independent feature extraction methods. More recently, transformer-based language models like BERT can also be pre-trained on multiple languages, making them intuitive candidates for cross-language classifiers which have not been used for this task yet. We perform extensive experiments to benchmark the performance of three different approaches to a smallscale cross-language authorship attribution experiment: (1) using language-independent features with traditional classification models, (2) using multilingual pre-trained language models, and (3) using machine translation to allow single-language classification. For the language-independent features, we utilize universal syntactic features like part-of-speech tags and dependency graphs, and multilingual BERT as a pre-trained language model. We use a small-scale social media comments dataset, closely reflecting practical scenarios. We show that applying machine translation drastically increases the performance of almost all approaches, and that the syntactic features in combination with the translation step achieve the best overall classification performance. In particular, we demonstrate that pre-trained language models are outperformed by traditional models in small scale authorship attribution problems for every language combination analyzed in this paper.
Abstract We find that the requirement of model interpretations to be faithful is vague and incomplete. With interpretation by textual highlights as a case study, we present several failure cases. Borrowing concepts from social science, we identify th
at the problem is a misalignment between the causal chain of decisions (causal attribution) and the attribution of human behavior to the interpretation (social attribution). We reformulate faithfulness as an accurate attribution of causality to the model, and introduce the concept of aligned faithfulness: faithful causal chains that are aligned with their expected social behavior. The two steps of causal attribution and social attribution together complete the process of explaining behavior. With this formalization, we characterize various failures of misaligned faithful highlight interpretations, and propose an alternative causal chain to remedy the issues. Finally, we implement highlight explanations of the proposed causal format using contrastive explanations.
Abstract We introduce an Edit-Based TransfOrmer with Repositioning (EDITOR), which makes sequence generation flexible by seamlessly allowing users to specify preferences in output lexical choice. Building on recent models for non-autoregressive seque
nce generation (Gu et al., 2019), EDITOR generates new sequences by iteratively editing hypotheses. It relies on a novel reposition operation designed to disentangle lexical choice from word positioning decisions, while enabling efficient oracles for imitation learning and parallel edits at decoding time. Empirically, EDITOR uses soft lexical constraints more effectively than the Levenshtein Transformer (Gu et al., 2019) while speeding up decoding dramatically compared to constrained beam search (Post and Vilar, 2018). EDITOR also achieves comparable or better translation quality with faster decoding speed than the Levenshtein Transformer on standard Romanian-English, English-German, and English-Japanese machine translation tasks.
The aim of this study was to reveal the impact of supervision and educational
attribution in improving the performance of the teachers of the first three grades and to
improve the reading skills of their students.
The study found that there were s
tatistically significant differences between the
tribal and remote applications in all sub-competencies and the total degree of teachers. The
differences were in favor of the post application. The mathematical averages for the post
application ranged from (3,41-3,88). The performance skills related to planning the lesson
(3.88), while the "performance skills related to the evaluation" ranked last with an average
of (3.41), and the general arithmetic mean of the fields (3.55). Table (1) shows that there
are statistically significant differences at the level of significance (0.05 = α) between the
tribal and remote applications in all sub-performance skills and the total score, and the
differences were in favor of the post-application of teachers. The results of the reading
skills test showed statistically significant differences between the tribal and remote
applications in all secondary competencies and the total score of students. The differences
were in favor of the post application. The mathematical averages for the post application
ranged between (2.03-3.65) For learning to read "ranked first with the highest average
score of (3.65), while" fourth level of learning to read "in the last place with an average of
(2,03), and the arithmetic mean of the total score (2.97). There were also statistically
significant differences at the level of significance (0.05) (α) between the tribal and remote
applications Performance and skills sub-class college, came the differences in favor of the
dimensional application for students.
It has been shown through the research on the subject of voluntary change
of the rules of attribution and selection of applicable law that the will has a
prominent role in the conflict of laws and the application of the law by
giving the parties t
he right to choose the most appropriate law governing
the legal relationship with a foreign element and enable them to determine
the fate of this legal relationship.
