United Arab Emirates

This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all LM parameters, we only tune a few new parameters while keeping the original LM parameters fixed. We establish this via reformulating KG completion as a &quot;fill-in-the-blank&#39;&#39; task, and introducing a parameter-lite encoder on top of the original LMs. We show that, by tuning far fewer parameters than finetuning, LMs transfer non-trivially to most tasks and reach competitiveness with prior state-of-the-art approaches. For instance, we outperform the fully finetuning approaches on a KG completion benchmark by tuning only 1% of the parameters.

EMNLP 2022

PALT: Parameter-Lite Transfer of Language Models for Knowledge Graph Completion

efficient method

pretrained language models

knowledge graph

This paper presents a parameter-lite transfer learning approach of pretrained language models (LM) for knowledge graph (KG) completion. Instead of finetuning, which modifies all LM parameters, we only tune a few new parameters while keeping the original LM parameters fixed. We establish this via reformulating KG completion as a "fill-in-the-blank'' task, and introducing a parameter-lite encoder on top of the original LMs. We show that, by tuning far fewer parameters than finetuning, LMs transfer non-trivially to most tasks and reach competitiveness with prior state-of-the-art approaches. For instance, we outperform the fully finetuning approaches on a KG completion benchmark by tuning only 1% of the parameters.

findings / work in progress

## Welcome to EMNLP 2022!
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EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Knowledge Base Question Answering (KBQA) involving complex reasoning is emerging as an important research direction. However, most KBQA systems struggle with generalizability, particularly on two dimensions: (a) across multiple knowledge bases, where existing KBQA approaches are typically tuned to a single knowledge base, and (b) across multiple reasoning types, where majority of datasets and systems have primarily focused on multi-hop reasoning. In this paper, we present SYGMA, a modular KBQA approach developed with goal of generalization across multiple knowledge bases and multiple reasoning types. To facilitate this, SYGMA is designed as two high level modules: 1) KB-agnostic question understanding module that remain common across KBs, and generates logic representation of the question with high level reasoning constructs that are extensible, and 2) KB-specific question mapping and answering module to address the KB-specific aspects of the answer extraction. We evaluated SYGMA on multiple datasets belonging to distinct knowledge bases (DBpedia and Wikidata) and distinct reasoning types (multi-hop and temporal). State-of-the-art or competitive performances achieved on those datasets demonstrate its generalization capability.

SYGMA: A System for Generalizable and Modular Question Answering Over Knowledge Bases

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.

SepLL: Separating Latent Class Labels from Weak Supervision Noise

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 "The capital of France is ---". However, these probes may be affected by the co-occurrence of target relation words and entities (e.g. "capital", "France" and "Paris") 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.

Probing Relational Knowledge in Language Models via Word Analogies

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.

Parameter-free Automatically Prompting: A Latent Pseudo Label Mapping Model for Prompt-based Learning

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.

Exploring Logographic Image for Chinese Aspect-based Sentiment Classification

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.

CAT-probing: A Metric-based Approach to Interpret How Pre-trained Models for Programming Language Attend Code Structure

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.

On the Impact of Temporal Concept Drift on Model Explanations

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 "almost correct" 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.

Text-Only Training for Image Captioning using Noise-Injected CLIP

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.

TINA: Textual Inference with Negation Augmentation

Argumentation is used by people both internally, by evaluating arguments and counterarguments to make sense of a situation and take a decision, and externally, e.g., in a debate, by exchanging arguments to reach an agreement or to promote an individual position. In this context, the assessment of the quality of the arguments is of extreme importance, as it strongly influences the evaluation of the overall argumentation, impacting on the decision making process. The automatic assessment of the quality of natural language arguments is recently attracting interest in the Argument Mining field. However, the issue of automatically assessing the quality of an argumentation largely remains a challenging unsolved task. Our contribution is twofold: first, we present a novel resource of 402 student persuasive essays, where three main quality dimensions (i.e., cogency, rhetoric, and reasonableness) have been annotated, leading to 1908 arguments tagged with quality facets; second, we address this novel task of argumentation quality assessment proposing a novel neural architecture based on graph embeddings, that combines both the textual features of the natural language arguments and the overall argument graph, i.e., considering also the support and attack relations holding among the arguments. Results on the persuasive essays dataset outperform state-of-the-art and standard baselines' performance.

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SYGMA: A System for Generalizable and Modular Question Answering Over Knowledge Bases

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