ymoslem/acl-abstracts huggingface dataset:
Adapters for Enhanced Modeling of Multilingual Knowledge and Text (2022)

Large language models appear to learn facts from the large text corpora they are trained on. Such facts are encoded implicitly within their many parameters, making it difficult to verify or manipulate what knowledge has been learned. Language models have recently been extended to multilingual language models (MLLMs), enabling knowledge to be learned across hundreds of languages. Meanwhile, knowledge graphs contain facts in an explicit triple format, which require careful and costly curation and are only available in a few high-resource languages, restricting their research and application. To address these issues, we propose to enhance MLLMs with knowledge from multilingual knowledge graphs (MLKGs) so as to tackle language and knowledge graph tasks across many languages, including low-resource ones. Specifically, we introducea lightweight adapter set to enhance MLLMs with cross-lingual entity alignment and facts from MLKGs for many languages. Experiments on common benchmarks show that such enhancement benefits both MLLMs and MLKGs, achieving: (1) comparable or improved performance for knowledge graph completion and entity alignment relative to baselines, especially for low-resource languages (for which knowledge graphs are unavailable); and (2) improved MLLM performance on language understanding tasks that require multilingual factual knowledge; all while maintaining performance on other general language tasks.
SepLL: Separating Latent Class Labels from Weak Supervision Noise (2022)

In the weakly supervised learning paradigm, labeling functions automatically assign heuristic, often noisy, labels to data samples. In this work, we provide a method for learning from weak labels by separating two types of complementary information associated with the labeling functions: information related to the target label and information specific to one labeling function only. Both types of information are reflected to different degrees by all labeled instances. In contrast to previous works that aimed at correcting or removing wrongly labeled instances, we learn a branched deep model that uses all data as-is, but splits the labeling function information in the latent space. Specifically, we propose the end-to-end model SepLL which extends a transformer classifier by introducing a latent space for labeling function specific and task-specific information. The learning signal is only given by the labeling functions matches, no pre-processing or label model is required for our method. Notably, the task prediction is made from the latent layer without any direct task signal. Experiments on Wrench text classification tasks show that our model is competitive with the state-of-the-art, and yields a new best average performance.
Probing Relational Knowledge in Language Models via Word Analogies (2022)

Understanding relational knowledge plays an integral part in natural language comprehension. When it comes to pre-trained language models (PLM), prior work has been focusing on probing relational knowledge this by filling the blanks in pre-defined prompts such as {\textquotedblleft}The capital of France is {---}''. However, these probes may be affected by the co-occurrence of target relation words and entities (e.g. {\textquotedblleft}capital{\textquotedblright}, {\textquotedblleft}France{\textquotedblright} and {\textquotedblleft}Paris{\textquotedblright}) in the pre-training corpus. In this work, we extend these probing methodologies leveraging analogical proportions as a proxy to probe relational knowledge in transformer-based PLMs without directly presenting the desired relation. In particular, we analysed the ability of PLMs to understand (1) the directionality of a given relation (e.g. Paris-France is not the same as France-Paris); (2) the ability to distinguish types on a given relation (both France and Japan are countries); and (3) the relation itself (Paris is the capital of France, but not Rome). Our results show how PLMs are extremely accurate at (1) and (2), but have clear room for improvement for (3). To better understand the reasons behind this behaviour and mistakes made by PLMs, we provide an extended quantitative analysis based on relevant factors such as frequency.
Semi-Supervised Lifelong Language Learning (2022)

Lifelong learning aims to accumulate knowledge and alleviate catastrophic forgetting when learning tasks sequentially. However, existing lifelong language learning methods only focus on the supervised learning setting. Unlabeled data, which can be easily accessed in real-world scenarios, are underexplored. In this paper, we explore a novel setting, semi-supervised lifelong language learning (SSLL), where a model learns sequentially arriving language tasks with both labeled and unlabeled data. We propose an unlabeled data enhanced lifelong learner to explore SSLL. Specially, we dedicate task-specific modules to alleviate catastrophic forgetting and design two modules to exploit unlabeled data: (1) a virtual supervision enhanced task solver is constructed on a teacher-student framework to mine the underlying knowledge from unlabeled data; and (2) a backward augmented learner is built to encourage knowledge transfer from newly arrived unlabeled data to previous tasks. Experimental results on various language tasks demonstrate our model`s effectiveness and superiority over competitive baselines under the new setting SSLL.
Parameter-free Automatically Prompting: A Latent Pseudo Label Mapping Model for Prompt-based Learning (2022)

Prompt-based learning has achieved excellent performance in few-shot learning by mapping the outputs of the pre-trained language model to the labels with the help of a label mapping component. Existing manual label mapping (MLM) methods achieve good results but heavily rely on expensive human knowledge. Automatic label mapping (ALM) methods that learn the mapping functions with extra parameters have shown their potentiality. However, no effective ALM model comparable to MLM methods is developed yet due to the limited data. In this paper, we propose a Latent Pseudo Label Mapping (LPLM) method that optimizes the label mapping without human knowledge and extra parameters. LPLM is built upon a probabilistic latent model and is iteratively self-improved with the EM-style algorithm. The empirical results demonstrate that our LPLM method is superior to the mainstream ALM methods and significantly outperforms the SOTA method in few-shot classification tasks. Moreover, LPLM also shows impressively better performance than the vanilla MLM method which requires extra task-specific prior knowledge.
Exploring Logographic Image for Chinese Aspect-based Sentiment Classification (2022)

In logographic languages like Chinese, word meanings are constructed using specific character formations, which can help to disambiguate word senses and are beneficial for sentiment classification. However, such knowledge is rarely explored in previous sentiment analysis methods. In this paper, we focus on exploring the logographic information for aspect-based sentiment classification in Chinese text. Specifically, we employ a logographic image to capture an internal morphological structure from the character sequence. The logographic image is also used to learn the external relations among context and aspect words. Furthermore, we propose a multimodal language model to explicitly incorporate a logographic image with review text for aspect-based sentiment classification in Chinese. Experimental results show that our method brings substantial performance improvement over strong baselines. The results also indicate that the logographic image is very important for exploring the internal structure and external relations from the character sequence.
On the Role of Bidirectionality in Language Model Pre-Training (2022)

Prior work on language model pre-training has explored different architectures and learning objectives, but differences in data, hyperparameters and evaluation make a principled comparison difficult. In this work, we focus on bidirectionality as a key factor that differentiates existing approaches, and present a comprehensive study of its role in next token prediction, text infilling, zero-shot priming and fine-tuning. We propose a new framework that generalizes prior approaches, including fully unidirectional models like GPT, fully bidirectional models like BERT, and hybrid models like CM3 and prefix LM. Our framework distinguishes between two notions of bidirectionality (bidirectional context and bidirectional attention) and allows us to control each of them separately. We find that the optimal configuration is largely application-dependent (e.g., bidirectional attention is beneficial for fine-tuning and infilling, but harmful for next token prediction and zero-shot priming). We train models with up to 6.7B parameters, and find differences to remain consistent at scale. While prior work on scaling has focused on left-to-right autoregressive models, our results suggest that this approach comes with some trade-offs, and it might be worthwhile to develop very large bidirectional models.
You Are What You Talk About: Inducing Evaluative Topics for Personality Analysis (2022)

Expressing attitude or stance toward entities and concepts is an integral part of human behavior and personality. Recently, evaluative language data has become more accessible with social media`s rapid growth, enabling large-scale opinion analysis. However, surprisingly little research examines the relationship between personality and evaluative language. To bridge this gap, we introduce the notion of evaluative topics, obtained by applying topic models to pre-filtered evaluative text from social media. We then link evaluative topics to individual text authors to build their evaluative profiles. We apply evaluative profiling to Reddit comments labeled with personality scores and conduct an exploratory study on the relationship between evaluative topics and Big Five personality facets, aiming for a more interpretable, facet-level analysis. Finally, we validate our approach by observing correlations consistent with prior research in personality psychology.
CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure (2022)

Code pre-trained models (CodePTMs) have recently demonstrated significant success in code intelligence. To interpret these models, some probing methods have been applied. However, these methods fail to consider the inherent characteristics of codes. In this paper, to address the problem, we propose a novel probing method CAT-probing to quantitatively interpret how CodePTMs attend code structure. We first denoise the input code sequences based on the token types pre-defined by the compilers to filter those tokens whose attention scores are too small. After that, we define a new metric CAT-score to measure the commonality between the token-level attention scores generated in CodePTMs and the pair-wise distances between corresponding AST nodes. The higher the CAT-score, the stronger the ability of CodePTMs to capture code structure. We conduct extensive experiments to integrate CAT-probing with representative CodePTMs for different programming languages. Experimental results show the effectiveness of CAT-probing in CodePTM interpretation. Our codes and data are publicly available at https://github.com/nchen909/CodeAttention.
Learning to Revise References for Faithful Summarization (2022)

In real-world scenarios with naturally occurring datasets, reference summaries are noisy and may contain information that cannot be inferred from the source text. On large news corpora, removing low quality samples has been shown to reduce model hallucinations. Yet, for smaller, and/or noisier corpora, filtering is detrimental to performance. To improve reference quality while retaining all data, we propose a new approach: to selectively re-write unsupported reference sentences to better reflect source data. We automatically generate a synthetic dataset of positive and negative revisions by corrupting supported sentences and learn to revise reference sentences with contrastive learning. The intensity of revisions is treated as a controllable attribute so that, at inference, diverse candidates can be over-generated-then-rescored to balance faithfulness and abstraction. To test our methods, we extract noisy references from publicly available MIMIC-III discharge summaries for the task of hospital-course summarization, and vary the data on which models are trained. According to metrics and human evaluation, models trained on revised clinical references are much more faithful, informative, and fluent than models trained on original or filtered data.
Towards Intention Understanding in Suicidal Risk Assessment with Natural Language Processing (2022)

Recent applications of natural language processing techniques to suicidal ideation detection and risk assessment frame the detection or assessment task as a text classification problem. Recent advances have developed many models, especially deep learning models, to boost predictive performance.Though the performance (in terms of aggregated evaluation scores) is improving, this position paper urges that better intention understanding is required for reliable suicidal risk assessment with computational methods. This paper reflects the state of natural language processing applied to suicide-associated text classification tasks, differentiates suicidal risk assessment and intention understanding, and points out potential limitations of sentiment features and pretrained language models in suicidal intention understanding.Besides, it urges the necessity for sequential intention understanding and risk assessment, discusses some critical issues in evaluation such as uncertainty, and studies the lack of benchmarks.
On the Impact of Temporal Concept Drift on Model Explanations (2022)

Explanation faithfulness of model predictions in natural language processing is typically evaluated on held-out data from the same temporal distribution as the training data (i.e. synchronous settings). While model performance often deteriorates due to temporal variation (i.e. temporal concept drift), it is currently unknown how explanation faithfulness is impacted when the time span of the target data is different from the data used to train the model (i.e. asynchronous settings). For this purpose, we examine the impact of temporal variation on model explanations extracted by eight feature attribution methods and three select-then-predict models across six text classification tasks. Our experiments show that (i) faithfulness is not consistent under temporal variations across feature attribution methods (e.g. it decreases or increases depending on the method), with an attention-based method demonstrating the most robust faithfulness scores across datasets; and (ii) select-then-predict models are mostly robust in asynchronous settings with only small degradation in predictive performance. Finally, feature attribution methods show conflicting behavior when used in FRESH (i.e. a select-and-predict model) and for measuring sufficiency/comprehensiveness (i.e. as post-hoc methods), suggesting that we need more robust metrics to evaluate post-hoc explanation faithfulness. Code will be made publicly available.
Text-Only Training for Image Captioning using Noise-Injected CLIP (2022)

We consider the task of image-captioning using only the CLIP model and additional text data at training time and no additional captioned images. Our approach relies on the fact that CLIP is trained to make visual and textual embeddings similar. Therefore, we only need to learn how to translate CLIP textual embeddings back into text, and we can learn how to do this by learning a decoder for the frozen CLIP text encoder using only text. We argue that this intuition is {\textquotedblleft}almost correct{\textquotedblright} because of a gap between the embedding spaces, and propose to rectify this via noise injection during training. We demonstrate the effectiveness of our approach by showing SOTA zero-shot image captioning across four benchmarks, including style transfer. Code, data, and models are available at https://github.com/DavidHuji/CapDec.
Improving Sharpness-Aware Minimization with Fisher Mask for Better Generalization on Language Models (2022)

Fine-tuning large pretrained language models on a limited training corpus usually suffers from poor generalization. Prior works show that the recently-proposed sharpness-aware minimization (SAM) optimization method can improve the model generalization. However, SAM adds a perturbation to each model parameter equally (but not all parameters contribute equally to the optimization of training), which we argue is sub-optimal and will lead to excessive computation. In this paper, we propose a novel optimization procedure, namely FSAM, which introduces a Fisher mask to improve the efficiency and performance of SAM. In short, instead of adding perturbation to all parameters, FSAM uses the Fisher information to identity the important parameters and formulates a Fisher mask to obtain the sparse perturbation, i.e., making the optimizer focus on these important parameters. Experiments on various tasks in GLUE and SuperGLUE benchmarks show that FSAM consistently outperforms the vanilla SAM by 0.67 1.98 average score among four different pretrained models. We also empirically show that FSAM works well in other complex scenarios, e.g., fine-tuning on generation tasks or limited training data. Encouragingly, when training data is limited, FSAM improves the SAM by a large margin, i.e., up to 15.1.
TINA: Textual Inference with Negation Augmentation (2022)

Transformer-based language models achieve state-of-the-art results on several natural language processing tasks. One of these is textual entailment, i.e., the task of determining whether a premise logically entails a hypothesis. However, the models perform poorly on this task when the examples contain negations. In this paper, we propose a new definition of textual entailment that captures also negation. This allows us to develop TINA (Textual Inference with Negation Augmentation), a principled technique for negated data augmentation that can be combined with the unlikelihood loss function.Our experiments with different transformer-based models show that our method can significantly improve the performance of the models on textual entailment datasets with negation {--} without sacrificing performance on datasets without negation.
Improving Bilingual Lexicon Induction with Cross-Encoder Reranking (2022)

Bilingual lexicon induction (BLI) with limited bilingual supervision is a crucial yet challenging task in multilingual NLP. Current state-of-the-art BLI methods rely on the induction of cross-lingual word embeddings (CLWEs) to capture cross-lingual word similarities; such CLWEs are obtained {\ensuremath{<}}b{\ensuremath{>}}1){\ensuremath{<}}/b{\ensuremath{>}} via traditional static models (e.g., VecMap), or {\ensuremath{<}}b{\ensuremath{>}}2){\ensuremath{<}}/b{\ensuremath{>}} by extracting type-level CLWEs from multilingual pretrained language models (mPLMs), or {\ensuremath{<}}b{\ensuremath{>}}3){\ensuremath{<}}/b{\ensuremath{>}} through combining the former two options. In this work, we propose a novel semi-supervised {\ensuremath{<}}i{\ensuremath{>}}post-hoc{\ensuremath{<}}/i{\ensuremath{>}} reranking method termed {\ensuremath{<}}b{\ensuremath{>}}BLICEr{\ensuremath{<}}/b{\ensuremath{>}} ({\ensuremath{<}}b{\ensuremath{>}}BLI{\ensuremath{<}}/b{\ensuremath{>}} with {\ensuremath{<}}b{\ensuremath{>}}C{\ensuremath{<}}/b{\ensuremath{>}}ross-{\ensuremath{<}}b{\ensuremath{>}}E{\ensuremath{<}}/b{\ensuremath{>}}ncoder {\ensuremath{<}}b{\ensuremath{>}}R{\ensuremath{<}}/b{\ensuremath{>}}eranking), applicable to any precalculated CLWE space, which improves their BLI capability. The key idea is to {\textquoteleft}extract' cross-lingual lexical knowledge from mPLMs, and then combine it with the original CLWEs. This crucial step is done via {\ensuremath{<}}b{\ensuremath{>}}1){\ensuremath{<}}/b{\ensuremath{>}} creating a word similarity dataset, comprising positive word pairs (i.e., true translations) and hard negative pairs induced from the original CLWE space, and then {\ensuremath{<}}b{\ensuremath{>}}2){\ensuremath{<}}/b{\ensuremath{>}} fine-tuning an mPLM (e.g., mBERT or XLM-R) in a cross-encoder manner to predict the similarity scores. At inference, we {\ensuremath{<}}b{\ensuremath{>}}3){\ensuremath{<}}/b{\ensuremath{>}} combine the similarity score from the original CLWE space with the score from the BLI-tuned cross-encoder. BLICEr establishes new state-of-the-art results on two standard BLI benchmarks spanning a wide spectrum of diverse languages: it substantially outperforms a series of strong baselines across the board. We also validate the robustness of BLICEr with different CLWEs.
Mixed-modality Representation Learning and Pre-training for Joint Table-and-Text Retrieval in OpenQA (2022)

Retrieving evidences from tabular and textual resources is essential for open-domain question answering (OpenQA), which provides more comprehensive information. However, training an effective dense table-text retriever is difficult due to the challenges of table-text discrepancy and data sparsity problem. To address the above challenges, we introduce an optimized OpenQA Table-Text Retriever (OTTeR) to jointly retrieve tabular and textual evidences. Firstly, we propose to enhance mixed-modality representation learning via two mechanisms: modality-enhanced representation and mixed-modality negative sampling strategy. Secondly, to alleviate data sparsity problem and enhance the general retrieval ability, we conduct retrieval-centric mixed-modality synthetic pre-training. Experimental results demonstrate that OTTeR substantially improves the performance of table-and-text retrieval on the OTT-QA dataset. Comprehensive analyses examine the effectiveness of all the proposed mechanisms. Besides, equipped with OTTeR, our OpenQA system achieves the state-of-the-art result on the downstream QA task, with 10.1{\%} absolute improvement in terms of the exact match over the previous best system.
The Effects of Corpus Choice and Morphosyntax on Multilingual Space Induction (2022)

In an effort to study the inductive biases of language models, numerous studies have attempted to use linguistically motivated tasks as a proxy of sorts, wherein performance on these tasks would imply an inductive bias towards a specific linguistic phenomenon. In this study, we attempt to analyse the inductive biases of language models with respect to natural language phenomena, in the context of building multilingual embedding spaces.We sample corpora from 2 sources in 15 languages and train language models on pseudo-bilingual variants of each corpus, created by duplicating each corpus and shifting token indices for half the resulting corpus. We evaluate the cross-lingual capabilities of these LMs, and show that while correlations with language families tend to be weak, other corpus-level characteristics, such as type-token ratio, tend to be more strongly correlated. Finally, we show that multilingual spaces can be built, albeit less effectively, even when additional destructive perturbations are applied to the training corpora, implying that (effectively) bag-of-words models also have an inductive bias that is sufficient for inducing multilingual spaces.