Reviewerly/exHarmony huggingface dataset:
Gold Doesn’t Always Glitter: Spectral Removal of Linear and Nonlinear Guarded Attribute Information

We describe a simple and effective method (Spectral Attribute removaL; SAL) to remove private or guarded information from neural representations. Our method uses matrix decomposition to project the input representations into directions with reduced covariance with the guarded information rather than maximal covariance as factorization methods normally use. We begin with linear information removal and proceed to generalize our algorithm to the case of nonlinear information removal using kernels. Our experiments demonstrate that our algorithm retains better main task performance after removing the guarded information compared to previous work. In addition, our experiments demonstrate that we need a relatively small amount of guarded attribute data to remove information about these attributes, which lowers the exposure to sensitive data and is more suitable for low-resource scenarios.
Improving the Generalizability of Collaborative Dialogue Analysis With Multi-Feature Embeddings

Conflict prediction in communication is integral to the design of virtual agents that support successful teamwork by providing timely assistance. The aim of our research is to analyze discourse to predict collaboration success. Unfortunately, resource scarcity is a problem that teamwork researchers commonly face since it is hard to gather a large number of training examples. To alleviate this problem, this paper introduces a multi-feature embedding (MFeEmb) that improves the generalizability of conflict prediction models trained on dialogue sequences. MFeEmb leverages textual, structural, and semantic information from the dialogues by incorporating lexical, dialogue acts, and sentiment features. The use of dialogue acts and sentiment features reduces performance loss from natural distribution shifts caused mainly by changes in vocabulary. This paper demonstrates the performance of MFeEmb on domain adaptation problems in which the model is trained on discourse from one task domain and applied to predict team performance in a different domain. The generalizability of MFeEmb is quantified using the similarity measure proposed by Bontonou et al. (2021). Our results show that MFeEmb serves as an excellent domain-agnostic representation for meta-pretraining a few-shot model on collaborative multiparty dialogues.
End-to-end Case-Based Reasoning for Commonsense Knowledge Base Completion

Pretrained language models have been shown to store knowledge in their parameters and have achieved reasonable performance in commonsense knowledge base completion (CKBC) tasks. However, CKBC is knowledge-intensive and it is reported that pretrained language models’ performance in knowledge-intensive tasks are limited because of their incapability of accessing and manipulating knowledge. As a result, we hypothesize that providing retrieved passages that contain relevant knowledge as additional input to the CKBC task will improve performance. In particular, we draw insights from Case-Based Reasoning (CBR) – which aims to solve a new problem by reasoning with retrieved relevant cases, and investigate the direct application of it to CKBC. On two benchmark datasets, we demonstrate through automatic and human evaluations that our End-to-end Case-Based Reasoning Framework (ECBRF) generates more valid, informative, and novel knowledge than the state-of-the-art COMET model for CKBC in both the fully supervised and few-shot settings. We provide insights on why previous retrieval-based methods only achieve merely the same performance with COMET. From the perspective of CBR, our framework addresses a fundamental question on whether CBR methodology can be utilized to improve deep learning models.
SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models

A common limitation of diagnostic tests for detecting social biases in NLP models is that they may only detect stereotypic associations that are pre-specified by the designer of the test. Since enumerating all possible problematic associations is infeasible, it is likely these tests fail to detect biases that are present in a model but not pre-specified by the designer. To address this limitation, we propose SODAPOP (SOcial bias Discovery from Answers about PeOPle), an approach for automatic social bias discovery in social commonsense question-answering. The SODAPOP pipeline generates modified instances from the Social IQa dataset (Sap et al., 2019b) by (1) substituting names associated with different demographic groups, and (2) generating many distractor answers from a masked language model. By using a social commonsense model to score the generated distractors, we are able to uncover the model’s stereotypic associations between demographic groups and an open set of words. We also test SODAPOP on debiased models and show the limitations of multiple state-of-the-art debiasing algorithms.
UScore: An Effective Approach to Fully Unsupervised Evaluation Metrics for Machine Translation

The vast majority of evaluation metrics for machine translation are supervised, i.e., (i) are trained on human scores, (ii) assume the existence of reference translations, or (iii) leverage parallel data. This hinders their applicability to cases where such supervision signals are not available. In this work, we develop fully unsupervised evaluation metrics. To do so, we leverage similarities and synergies between evaluation metric induction, parallel corpus mining, and MT systems. In particular, we use an unsupervised evaluation metric to mine pseudo-parallel data, which we use to remap deficient underlying vector spaces (in an iterative manner) and to induce an unsupervised MT system, which then provides pseudo-references as an additional component in the metric. Finally, we also induce unsupervised multilingual sentence embeddings from pseudo-parallel data. We show that our fully unsupervised metrics are effective, i.e., they beat supervised competitors on 4 out of our 5 evaluation datasets. We make our code publicly available.
A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing

Many natural language processing (NLP) tasks are naturally imbalanced, as some target categories occur much more frequently than others in the real world. In such scenarios, current NLP models tend to perform poorly on less frequent classes. Addressing class imbalance in NLP is an active research topic, yet, finding a good approach for a particular task and imbalance scenario is difficult.In this survey, the first overview on class imbalance in deep-learning based NLP, we first discuss various types of controlled and real-world class imbalance.Our survey then covers approaches that have been explicitly proposed for class-imbalanced NLP tasks or, originating in the computer vision community, have been evaluated on them.We organize the methods by whether they are based on sampling, data augmentation, choice of loss function, staged learning, or model design.Finally, we discuss open problems and how to move forward.
Hate Speech Classifiers are Culturally Insensitive

Increasingly, language models and machine translation are becoming valuable tools to help people communicate with others from diverse cultural backgrounds. However, current language models lack cultural awareness because they are trained on data representing only the culture within the dataset. This presents a problem in the context of hate speech classification, where cultural awareness is especially critical. This study aims to quantify the cultural insensitivity of three monolingual (Korean, English, Arabic) hate speech classifiers by evaluating their performance on translated datasets from the other two languages. Our research has revealed that hate speech classifiers evaluated on datasets from other cultures yield significantly lower F1 scores, up to almost 50%. In addition, they produce considerably higher false negative rates, with a magnitude up to five times greater, demonstrating the extent of the cultural gap. The study highlights the severity of cultural insensitivity of language models in hate speech classification.
Deep learning models for real-life human activity recognition from smartphone sensor data

Nowadays, the field of human activity recognition (HAR) is a remarkably hot topic within the scientific community. Given the low cost, ease of use and high accuracy of the sensors from different wearable devices and smartphones, more and more researchers are opting to do their bit in this area. However, until very recently, all the work carried out in this field was done in laboratory conditions, with very few similarities with our daily lives. This paper will focus on this new trend of integrating all the knowledge acquired so far into a real-life environment. Thus, a dataset already published following this philosophy was used. In this way, this work aims to be able to identify the different actions studied there. In order to perform this classification, this paper explores new designs and architectures for models inspired by the ones which have yielded the best results in the literature. More specifically, different configurations of Convolutional Neural Networks (CNN) and Long-Short Term Memory (LSTM) have been tested, but on real-life conditions instead of laboratory ones. It is worth mentioning that the hybrid models formed from these techniques yielded the best results, with a peak accuracy of 94.80% on the dataset used.
Using Punctuation as an Adversarial Attack on Deep Learning-Based NLP Systems: An Empirical Study

This work empirically investigates punctuation insertions as adversarial attacks on NLP systems. Data from experiments on three tasks, five datasets, and six models with four attacks show that punctuation insertions, when limited to a few symbols (apostrophes and hyphens), are a superior attack vector compared to character insertions due to 1) a lower after-attack accuracy (Aaft-atk) than alphabetical character insertions; 2) higher semantic similarity between the resulting and original texts; and 3) a resulting text that is easier and faster to read as assessed with the Test of Word Reading Efficiency (TOWRE)). The tests also indicate that 4) grammar checking does not mitigate punctuation insertions and 5) punctuation insertions outperform word-level attacks in settings with a limited number of word synonyms and queries to the victim’s model. Our findings indicate that inserting a few punctuation types that result in easy-to-read samples is a general attack mechanism. In light of this threat, we assess the impact of punctuation insertions, potential mitigations, the mitigation’s tradeoffs, punctuation insertion’s worst-case scenarios and summarize our findings in a qualitative casual map, so that developers can design safer, more secure systems.
Self-Supervised Unimodal Label Generation Strategy Using Recalibrated Modality Representations for Multimodal Sentiment Analysis

While multimodal sentiment analysis (MSA) has gained much attention over the last few years, the main focus of most work on MSA has been limited to constructing multimodal representations that capture interactions between different modalities in a single task. This was largely due to a lack of unimodal annotations in MSA benchmark datasets. However, training a model using only multimodal representations can lead to suboptimal performance due to insufficient learning of each uni-modal representation. In this work, to fully optimize learning representations from multimodal data, we propose SUGRM which jointly trains multimodal and unimodal tasks using recalibrated features. The features are recalibrated such that the model learns to weight the features differently based on the features of other modalities. Further, to leverage unimodal tasks, we auto-generate unimodal annotations via a unimodal label generation module (ULGM). The experiment results on two benchmark datasets demonstrate the efficacy of our framework.
Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding

Mengnan Du, Subhabrata Mukherjee, Yu Cheng, Milad Shokouhi, Xia Hu, Ahmed Hassan Awadallah. Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics. 2023.
ICA-Proto: Iterative Cross Alignment Prototypical Network for Incremental Few-Shot Relation Classification

In the task of incremental few-shot relation classification, model performance is always limited by the incompatibility between the base feature embedding space and the novel feature embedding space. To tackle the issue, we propose a novel model named ICA-Proto: Iterative Cross Alignment prototypical network. Specifically, we incorporate the query representation into the encoding of novel prototypes and utilize the query-aware prototypes to update the query representation at the same time. Further, we implement the above process iteratively to achieve more interaction. In addition, a novel prototype quadruplet loss is designed to regulate the spatial distributions of embedding space, so as to make it easier for the relation classification. Experimental results on two benchmark datasets demonstrate that ICA-Proto significantly outperforms the state-of-the-art baseline model.
Exploring the Reasons for Non-generalizability of KBQA systems

Recent research has demonstrated impressive generalization capabilities of several Knowledge Base Question Answering (KBQA) models on the GrailQA dataset. We inspect whether these models can generalize to other datasets in a zero-shot setting. We notice a significant drop in performance and investigate the causes for the same. We observe that the models are dependent not only on the structural complexity of the questions, but also on the linguistic styles of framing a question. Specifically, the linguistic dimensions corresponding to explicitness, readability, coherence, and grammaticality have a significant impact on the performance of state-of-the-art KBQA models. Overall our results showcase the brittleness of such models and the need for creating generalizable systems.
Learning the Effects of Physical Actions in a Multi-modal Environment

Large Language Models (LLMs) handle physical commonsense information inadequately. As a result of being trained in a disembodied setting, LLMs often fail to predict an action’s outcome in a given environment. However, predicting the effects of an action before it is executed is crucial in planning, where coherent sequences of actions are often needed to achieve a goal. Therefore, we introduce the multi-modal task of predicting the outcomes of actions solely from realistic sensory inputs (images and text). Next, we extend an LLM to model latent representations of objects to better predict action outcomes in an environment. We show that multi-modal models can capture physical commonsense when augmented with visual information. Finally, we evaluate our model’s performance on novel actions and objects and find that combining modalities help models to generalize and learn physical commonsense reasoning better.
Unsupervised Keyphrase Extraction via Interpretable Neural Networks

Keyphrase extraction aims at automatically extracting a list of “important” phrases representing the key concepts in a document. Prior approaches for unsupervised keyphrase extraction resorted to heuristic notions of phrase importance via embedding clustering or graph centrality, requiring extensive domain expertise. Our work presents a simple alternative approach which defines keyphrases as document phrases that are salient for predicting the topic of the document. To this end, we propose INSPECT—an approach that uses self-explaining models for identifying influential keyphrases in a document by measuring the predictive impact of input phrases on the downstream task of the document topic classification. We show that this novel method not only alleviates the need for ad-hoc heuristics but also achieves state-of-the-art results in unsupervised keyphrase extraction in four datasets across two domains: scientific publications and news articles.
Automatic Evaluation and Analysis of Idioms in Neural Machine Translation

A major open problem in neural machine translation (NMT) is the translation of idiomatic expressions, such as “under the weather”. The meaning of these expressions is not composed by the meaning of their constituent words, and NMT models tend to translate them literally (i.e., word-by-word), which leads to confusing and nonsensical translations. Research on idioms in NMT is limited and obstructed by the absence of automatic methods for quantifying these errors. In this work, first, we propose a novel metric for automatically measuring the frequency of literal translation errors without human involvement. Equipped with this metric, we present controlled translation experiments with models trained in different conditions (with/without the test-set idioms) and across a wide range of (global and targeted) metrics and test sets. We explore the role of monolingual pretraining and find that it yields substantial targeted improvements, even without observing any translation examples of the test-set idioms. In our analysis, we probe the role of idiom context. We find that the randomly initialized models are more local or “myopic” as they are relatively unaffected by variations of the idiom context, unlike the pretrained ones.
Representation biases in sentence transformers

Variants of the BERT architecture specialised for producing full-sentence representations often achieve better performance on downstream tasks than sentence embeddings extracted from vanilla BERT. However, there is still little understanding of what properties of inputs determine the properties of such representations. In this study, we construct several sets of sentences with pre-defined lexical and syntactic structures and show that SOTA sentence transformers have a strong nominal-participant-set bias: cosine similarities between pairs of sentences are more strongly determined by the overlap in the set of their noun participants than by having the same predicates, lengthy nominal modifiers, or adjuncts. At the same time, the precise syntactic-thematic functions of the participants are largely irrelevant.
Multilingual Representation Distillation with Contrastive Learning

Multilingual sentence representations from large models encode semantic information from two or more languages and can be used for different cross-lingual information retrieval and matching tasks. In this paper, we integrate contrastive learning into multilingual representation distillation and use it for quality estimation of parallel sentences (i.e., find semantically similar sentences that can be used as translations of each other). We validate our approach with multilingual similarity search and corpus filtering tasks. Experiments across different low-resource languages show that our method greatly outperforms previous sentence encoders such as LASER, LASER3, and LaBSE.
Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering

To explain the predicted answers and evaluate the reasoning abilities of models, several studies have utilized underlying reasoning (UR) tasks in multi-hop question answering (QA) datasets. However, it remains an open question as to how effective UR tasks are for the QA task when training models on both tasks in an end-to-end manner. In this study, we address this question by analyzing the effectiveness of UR tasks (including both sentence-level and entity-level tasks) in three aspects: (1) QA performance, (2) reasoning shortcuts, and (3) robustness. While the previous models have not been explicitly trained on an entity-level reasoning prediction task, we build a multi-task model that performs three tasks together: sentence-level supporting facts prediction, entity-level reasoning prediction, and answer prediction. Experimental results on 2WikiMultiHopQA and HotpotQA-small datasets reveal that (1) UR tasks can improve QA performance. Using four debiased datasets that are newly created, we demonstrate that (2) UR tasks are helpful in preventing reasoning shortcuts in the multi-hop QA task. However, we find that (3) UR tasks do not contribute to improving the robustness of the model on adversarial questions, such as sub-questions and inverted questions. We encourage future studies to investigate the effectiveness of entity-level reasoning in the form of natural language questions (e.g., sub-question forms).
Cycle-Consistent Weakly Supervised Visual Grounding With Individual and Contextual Representations

Visual grounding, aiming to align image regions with textual queries, is a fundamental task for cross-modal learning. We study the weakly supervised visual grounding, where only image-text pairs at a coarse-grained level are available. Due to the lack of fine-grained correspondence information, existing approaches often encounter matching ambiguity. To overcome this challenge, we introduce the cycle consistency constraint into region-phrase pairs, which strengthens correlated pairs and weakens unrelated pairs. This cycle pairing makes use of the bidirectional association between image regions and text phrases to alleviate matching ambiguity. Furthermore, we propose a parallel grounding framework, where backbone networks and subsequent relation modules extract individual and contextual representations to calculate context-free and context-aware similarities between regions and phrases separately. Those two representations characterize visual/linguistic individual concepts and inter-relationships, respectively, and then complement each other to achieve cross-modal alignment. The whole framework is trained by minimizing an image-text contrastive loss and a cycle consistency loss. During inference, the above two similarities are fused to give the final region-phrase matching score. Experiments on five popular datasets about visual grounding demonstrate a noticeable improvement in our method. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Evergrow/WSVG</uri> .
Design and Implementation on Multi-Function Smart Wheelchair

With the increase of population aging, chronic diseases and accidental injuries, more and more people are facing the plight of diminished or even lost walking ability. As a kind of mobile service robot, the smart wheelchair has strong environmental adaptability, smooth motion control and friendly human-computer interaction experience, and is an indispensable tool in rehabilitation engineering and elderly assistance engineering, which has important research value and social significance. In this paper, the system structure framework of the multi-function smart wheelchair and the specific technical scheme of each module are formulated based on the modular design idea, and the intelligent control hardware platform with Arduino UNO controller as the core is built to complete the control software writing, and an intelligent wheelchair for the elderly and disabled guardianship is designed. This multi-function smart wheelchair combined with IoT technology can realize distance navigation, obstacle avoidance, medication reminder, sign measurement, remote monitoring, GPS positioning, and upload information through OneNET cloud platform, which can view the location of the wheelchair and the safety status of the elderly from the map in real time in the cell phone APP and PC backstage, in order to improve the control and monitoring capability of the wheelchair.