PInKS: Preconditioned Commonsense Inference with Minimal Supervision

Reasoning with preconditions such as "glass can be used for drinking water unless the glass is shattered" remains an open problem for language models. The main challenge lies in the scarcity of preconditions data and the model's lack of support for such reasoning. We present PInKS, Preconditioned Commonsense Inference with WeaK Supervision, an improved model for reasoning with preconditions through minimum supervision. We show, both empirically and theoretically, that PInKS improves the results on benchmarks focused on reasoning with the preconditions of commonsense knowledge (up to 40% Macro-F1 scores). We further investigate PInKS through PAC-Bayesian informativeness analysis, precision measures, and ablation study.Comment: AACL 202

Dense Retrieval as Indirect Supervision for Large-space Decision Making

Many discriminative natural language understanding (NLU) tasks have large label spaces. Learning such a process of large-space decision making is particularly challenging due to the lack of training instances per label and the difficulty of selection among many fine-grained labels. Inspired by dense retrieval methods for passage finding in open-domain QA, we propose a reformulation of large-space discriminative NLU tasks as a learning-to-retrieve task, leading to a novel solution named Dense Decision Retrieval (DDR ). Instead of predicting fine-grained decisions as logits, DDR adopts a dual-encoder architecture that learns to predict by retrieving from a decision thesaurus. This approach not only leverages rich indirect supervision signals from easy-to-consume learning resources for dense retrieval, it also leads to enhanced prediction generalizability with a semantically meaningful representation of the large decision space. When evaluated on tasks with decision spaces ranging from hundreds to hundred-thousand scales, DDR outperforms strong baselines greatly by 27.54% in P@1 on two extreme multi-label classification tasks, 1.17% in F1 score ultra-fine entity typing, and 1.26% in accuracy on three few-shot intent classification tasks on average. Code and resources are available at https://github.com/luka-group/DDRComment: EMNLP 2023 (Findings

Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?

Two key obstacles in biomedical relation extraction (RE) are the scarcity of annotations and the prevalence of instances without explicitly pre-defined labels due to low annotation coverage. Existing approaches, which treat biomedical RE as a multi-class classification task, often result in poor generalization in low-resource settings and do not have the ability to make selective prediction on unknown cases but give a guess from seen relations, hindering the applicability of those approaches. We present NBR, which converts biomedical RE as natural language inference formulation through indirect supervision. By converting relations to natural language hypotheses, NBR is capable of exploiting semantic cues to alleviate annotation scarcity. By incorporating a ranking-based loss that implicitly calibrates abstinent instances, NBR learns a clearer decision boundary and is instructed to abstain on uncertain instances. Extensive experiments on three widely-used biomedical RE benchmarks, namely ChemProt, DDI and GAD, verify the effectiveness of NBR in both full-set and low-resource regimes. Our analysis demonstrates that indirect supervision benefits biomedical RE even when a domain gap exists, and combining NLI knowledge with biomedical knowledge leads to the best performance gains.Comment: 16 pages; ACL 2023; code in https://github.com/luka-group/NLI_as_Indirect_Supervisio

Do Language Models Learn about Legal Entity Types during Pretraining?

Language Models (LMs) have proven their ability to acquire diverse linguistic knowledge during the pretraining phase, potentially serving as a valuable source of incidental supervision for downstream tasks. However, there has been limited research conducted on the retrieval of domain-specific knowledge, and specifically legal knowledge. We propose to explore the task of Entity Typing, serving as a proxy for evaluating legal knowledge as an essential aspect of text comprehension, and a foundational task to numerous downstream legal NLP applications. Through systematic evaluation and analysis and two types of prompting (cloze sentences and QA-based templates) and to clarify the nature of these acquired cues, we compare diverse types and lengths of entities both general and domain-specific entities, semantics or syntax signals, and different LM pretraining corpus (generic and legal-oriented) and architectures (encoder BERT-based and decoder-only with Llama2). We show that (1) Llama2 performs well on certain entities and exhibits potential for substantial improvement with optimized prompt templates, (2) law-oriented LMs show inconsistent performance, possibly due to variations in their training corpus, (3) LMs demonstrate the ability to type entities even in the case of multi-token entities, (4) all models struggle with entities belonging to sub-domains of the law (5) Llama2 appears to frequently overlook syntactic cues, a shortcoming less present in BERT-based architectures. The code of the experiments is available at https://github.com/clairebarale/ probing_legal_entity_types

Fishers’ Perception on the Interaction between Dolphins and Fishing Activities in Italian and Croatian Waters

Interactions between fishing and dolphins can be detrimental, since on one hand dolphins can be lethally entangled by nets and trawls, and on the other dolphins can predate fish caught by nets. For dolphins, this interaction can be dangerous as they can be wounded or accidentally killed; for fishers, the predation of their catch results in economic losses due to reduced quantity and/or quality of catches and damage to fishing gear. During July and November 2020, we surveyed the “dolphin–fisheries conflict” through compiling 209 fisher interviews from nine locations in Italy and Croatia. Fishers mentioned the common bottlenose dolphin (Tursiops truncatus) as the species primarily interacting with fishing, with the major issue being catch damage by predation. The interaction probability varied among gears and seasons, with some fishing activities (e.g., passive nets) more affected than others (e.g., bottom trawls), especially in terms of economic loss (1000–10,000 €/year on average). More than 70% of the fishers claimed that dolphin populations have increased over the last 10 years, in different degrees and based on different areas. Dolphin bycatch rates are generally low; however, 34.6% of respondents reported having captured at least one dolphin during their career. The fishers’ attitude towards acoustic deterrents (“pingers”) as a mitigation measure revealed that few of them were aware of these devices or were using them

Product liability for ADAS; legal and human factors perspectives

A variety of Advanced Driver Assistance Systems (ADAS) has been and is still being developed, aiming to make car driving more comfortable and safe, while at the same time enhancing traffic efficiency. However, the successful implementation of ADAS is affected by a variety of technical and non-technical issues, one of them being possible implications in the field of legal liability. Potential liability of system developers and car manufacturers is often labelled as a barrier for the rapid deployment of new technology. In the present contribution the European Product Liability Directive’s concept of a defective product is described and analysed from both a legal and a human factors perspective. In legal debates concerning product liability, generally two different approaches can be distinguished, one which is based on consumer expectations and a second which focuses, rather, on a risk-benefit analysis. As will be explained, the two may be seen as complementary and not as being mutually exclusive.Both tests can only be properly applied with the help of human factor expertise