Evaluating corporate sustainability performance is essential to drive sustainable business practices, amid the need for a more sustainable economy. However, this is hindered by the complexity and volume of corporate sustainability data (i.e. sustainability disclosures), not least by the effectiveness of the NLP tools used to analyse them. To this end, we identify three primary challenges - immateriality, complexity, and subjectivity, that exacerbate the difficulty of extracting insights from sustainability disclosures. To address these issues, we introduce ESGSenticNet, a publicly available knowledge base for sustainability analysis. ESGSenticNet is constructed from a neurosymbolic framework that integrates specialised concept parsing, GPT-4o inference, and semi-supervised label propagation, together with a hierarchical taxonomy. This approach culminates in a structured knowledge base of 44k knowledge triplets - ('halve carbon emission', supports, 'emissions control'), for effective sustainability analysis. Experiments indicate that ESGSenticNet, when deployed as a lexical method, more effectively captures relevant and actionable sustainability information from sustainability disclosures compared to state of the art baselines. Besides capturing a high number of unique ESG topic terms, ESGSenticNet outperforms baselines on the ESG relatedness and ESG action orientation of these terms by 26% and 31% respectively. These metrics describe the extent to which topic terms are related to ESG, and depict an action toward ESG. Moreover, when deployed as a lexical method, ESGSenticNet does not require any training, possessing a key advantage in its simplicity for non-technical stakeholders.

The syntactic structure of a sentence can be described as a tree that indicates the syntactic relationships between words. In spite of significant progress in unsupervised methods that retrieve the syntactic structure of sentences, guessing the right direction of edges is still a challenge. As in a syntactic dependency structure edges are oriented away from the root, the challenge of guessing the right direction can be reduced to finding an undirected tree and the root. The limited performance of current unsupervised methods demonstrates the lack of a proper understanding of what a root vertex is from first principles. We consider an ensemble of centrality scores, some that only take into account the free tree (non-spatial scores) and others that take into account the position of vertices (spatial scores). We test the hypothesis that the root vertex is an important or central vertex of the syntactic dependency structure. We confirm that hypothesis and find that the best performance in guessing the root is achieved by novel scores that only take into account the position of a vertex and that of its neighbours. We provide theoretical and empirical foundations towards a universal notion of rootness from a network science perspective.

Originality Improving the translation procedure by leveraging error detection on the target programming language is not a new idea, even though the authors do not seem to be aware of this. In semantic parsing, this has been used quite a bit over the last year, under the name of "execution-guided decoding" (see for example the WikiSQL leaderboard, where the top 4 entries currently use this). I believe this first appeared in "Execution-Guided Neural Program Decoding" (Wang et al, 2018); the idea is closest to the prefix-based pruning idea in this submission. This leaves the new "multiclass classification" error localization technique as original contribution, as well as the new dataset. Clarity The core contribution of the paper is a new algorithm, which is only described in prose; I would have preferred pseudo-code here, which would have been more concise.

The CFQ dataset designed for testing compositional generalization is really challenging. The presented results in this paper on CFQ are impressive. However, as pointed out by Reviewer #4, the proposed method relies on many dataset-specific design, and the technical novelty is incremental compared to prior work on semantic parsing. The work will be much more convincing if it can also be validated on another dataset.

Cross-lingual transfer is a popular approach to increase the amount of training data for NLP tasks in a low-resource context. However, the best strategy to decide which cross-lingual data to include is unclear. Prior research often focuses on a small set of languages from a few language families and/or a single task. It is still an open question how these findings extend to a wider variety of languages and tasks. In this work, we analyze cross-lingual transfer for 266 languages from a wide variety of language families. Moreover, we include three popular NLP tasks: POS tagging, dependency parsing, and topic classification. Our findings indicate that the effect of linguistic similarity on transfer performance depends on a range of factors: the NLP task, the (mono- or multilingual) input representations, and the definition of linguistic similarity.

The paper proposes a new method for compositional generalization in sequence-to-sequence tasks. The basic idea is to have a symbolic stack machine (capable of compositionally manipulating sequences) that is controlled by a neural network. The method gets perfect accuracy on an existing compositional generalization dataset, a small-scale English-French machine translation task, and a grammar parsing task. Pros: Novel architecture Attractive way of providing inductive bias without hardcoding too much knowledge The paper is well-written Strong experimental results in the domains considered Cons: The paper could do more by the way of providing insights about why the model works. The reviewers appreciated the clarifications provided in the author feedback.

The goal of this paper is to output a set of 3D bounding boxes and set of dominant planes for a scene depicted in a single image. The key insight is to incorporate stability constraints in the 3D layout, i.e., the reconstructed 3D boxes should not move too far under simulation (in Bullet) with physical forces (gravity, friction). Parameters for 3D boxes are regressed using a modified R-CNN training loss and dominant planes for the walls and floors are regressed via a RNN. A stability criterion is used to update the output 3D scene (via REINFORCE) where the predicted 3D layout is run through Bullet simulator and 3D displacements are checked. Results are shown on synthetic (SUNCG, SceneNet RGB-D) and real (SUN RGB-D) datasets, out-performing the factored 3D approach of [Tulsiani18].

Recent work has shown that generation from a prompted or fine-tuned language model can perform well at semantic parsing when the output is constrained to be a valid semantic representation. We introduce BenchCLAMP, a Benchmark to evaluate Constrained LAnguage Model Parsing, that includes context-free grammars for seven semantic parsing datasets and two syntactic parsing datasets with varied output meaning representations, as well as a constrained decoding interface to generate only valid outputs covered by these grammars. We provide low, medium, and high resource splits for each dataset, allowing accurate comparison of various language models under different data regimes. Our benchmark supports evaluation of language models using prompt-based learning as well as fine-tuning. Our experiments show that encoder-decoder pretrained language models can achieve similar performance or even surpass state-of-the-art methods for both syntactic and semantic parsing when the model output is constrained to be valid.

We focus on the weakly-supervised audio-visual video parsing task (AVVP), which aims to identify and locate all the events in audio/visual modalities. Previous works only concentrate on video-level overall label denoising across modalities, but overlook the segment-level label noise, where adjacent video segments (i.e., 1-second video clips) may contain different events. However, recognizing events on the segment is challenging because its label could be any combination of events that occur in the video. To address this issue, we consider tackling AVVP from the language perspective, since language could freely describe how various events appear in each segment beyond fixed labels. Specifically, we design language prompts to describe all cases of event appearance for each video.

The audio-visual video parsing task aims to parse a video into modality- and category-aware temporal segments. Previous work mainly focuses on weakly-supervised approaches, which learn from video-level event labels. During training, they do not know which modality perceives and meanwhile which temporal segment contains the video event. Since there is no explicit grouping in the existing frameworks, the modality and temporal uncertainties make these methods suffer from false predictions. For instance, segments in the same category could be predicted in different event classes.