United States

Legal Judgment Prediction (LJP) is a key problem in legal artificial intelligence, which is aimed to predict a law case&#39;s judgment based on a given text describing the facts of the law case. Most of the previous work treats LJP as a text classification task and generally adopts deep neural networks (DNNs) based methods to solve it.
However, existing DNNs based work is data-hungry and hard to explain which legal knowledge is based on to make such a prediction.
Thus, injecting legal knowledge into neural networks to interpret the model and improve performance remains a significant problem.
In this paper, we propose to represent declarative legal knowledge as a set of first-order logic rules and integrate these logic rules into a co-attention network-based model explicitly. The use of logic rules enhances neural networks with explicit logical reason capabilities and makes the model more interpretable. We take the civil loan scenario as a case study and demonstrate the effectiveness of the proposed method through comprehensive experiments and analysis conducted on the collected dataset.

AAAI 2021

Judgment Prediction via Injecting Legal Knowledge into Neural Networks

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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EMNLP 2023

EMNLP 2023 took place in Singapore from Dec 6th to Dec 10th, 2023.

Legal Judgment Prediction (LJP) is a key problem in legal artificial intelligence, which is aimed to predict a law case's judgment based on a given text describing the facts of the law case. Most of the previous work treats LJP as a text classification task and generally adopts deep neural networks (DNNs) based methods to solve it.
However, existing DNNs based work is data-hungry and hard to explain which legal knowledge is based on to make such a prediction.
Thus, injecting legal knowledge into neural networks to interpret the model and improve performance remains a significant problem.
In this paper, we propose to represent declarative legal knowledge as a set of first-order logic rules and integrate these logic rules into a co-attention network-based model explicitly. The use of logic rules enhances neural networks with explicit logical reason capabilities and makes the model more interpretable. We take the civil loan scenario as a case study and demonstrate the effectiveness of the proposed method through comprehensive experiments and analysis conducted on the collected dataset.

technical paper

The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. 

In this paper we give a structural characterization and extend the tractability frontier of the Simple Temporal Problem (STP) by defining the class of the Extended Simple Temporal Problem (ESTP), which augments STP with strict inequalities and monotone Boolean formulae on inequations (i.e., formulae involving the operations of conjunction, disjunction and parenthesization). A polynomial-time algorithm is provided to solve ESTP, faster than previous state-of-the-art algorithms for other extensions of STP that had been considered in the literature, all encompassed by ESTP.  We show the practical competitiveness of our approach through a proof-of-concept implementation and an experimental evaluation involving also state-of-the-art SMT solvers.

Faster and Better Simple Temporal Problems

In Text-to-AMR parsing, current state-of-the-art semantic parsers use cumbersome pipelines integrating several different modules or components, and exploit graph recategorization, i.e., a set of content-specific heuristics that are developed on the basis of the training set. However, the generalizability of graph recategorization in an out-of-distribution setting is unclear. In contrast, state-of-the-art AMR-to-Text generation, which can be seen as the inverse to parsing, is based on simpler seq2seq. In this paper, we cast Text-to-AMR and AMR-to-Text as a symmetric transduction task and show that by devising a careful graph linearization and extending a pretrained encoder-decoder model, it is possible to obtain state-of-the-art performances in both tasks using the very same seq2seq approach, i.e., SPRING (Symmetric PaRsIng aNd Generation). Our model does not require complex pipelines, nor heuristics built on heavy assumptions. In fact, we drop the need for graph recategorization, showing that this technique is actually harmful outside of the standard benchmark. Finally, we outperform the previous state of the art on the English AMR 2.0 dataset by a large margin: on Text-to-AMR we obtain an improvement of 3.6 Smatch points, while on AMR-to-Text we outperform the state of the art by 11.2 BLEU points. 
We release the software at github.com/SapienzaNLP/spring.

One SPRING to Rule Them Both: Symmetric AMR Semantic Parsing and Generation without a Complex Pipeline

Dung's abstract Argumentation Framework (AF)
has emerged as a central formalism in formal argumentation.
Key aspects of the success and popularity of Dung's framework include
its simplicity and expressiveness.
Integrity constraints help to express domain knowledge in a compact 
and natural way, thus keeping easy the modeling task
even for problems that otherwise would be hard to encode within an AF.
In this paper, after providing an intuitive semantics 
based on Lukasiewicz's logic for AFs with (strong) constraints,
called Constrained AFs (CAFs),
we propose Weak constrained AFs (WAFs) 
that enhance CAFs with weak constraints. 
Intuitively, these constraints can be used to find ``optimal'' 
solutions to problems defined through CAFs.
We provide a detailed complexity analysis of CAFs and WAFs,
showing that strong constraints do not increase the expressive 
power of AFs in most cases, 
while weak constraints systematically increase the expressive 
power of CAFs under several well-known argumentation semantics.

Argumentation Frameworks with Strong and Weak Constraints: Semantics and Complexity

Joint extraction of entities and relations focuses on detecting entity pairs and their relations simultaneously with a unified model. Based on the extraction order, previous works mainly solve this task through relation-last, relation-first and relation-middle manner. However, these methods still suffer from the template-dependency, non-entity detection and non-predefined relation prediction problem. To overcome these challenges, in this paper, we propose a unified multi-task learning framework to divide the task into three interacted sub-tasks. Specifically, we first introduce the type-attentional method for subject extraction to provide prior type information explicitly. Then, the subject-aware relation prediction is presented to select useful relations based on the combination of global and local semantics. Third, we propose a question generation based QA method for object extraction to obtain diverse queries automatically. Notably, our method detects subjects or objects without relying on NER models and thus it is capable of dealing with the non-entity scenario. Finally, three sub-tasks are integrated into a unified model through parameter sharing. Extensive experiments demonstrate that the proposed framework outperforms all the baseline methods on two benchmark datasets, and further achieve excellent performance for non-predefined relations.

A Unified Multi-Task Learning Framework for Joint Extraction of Entities and Relations

A critical concern in data-driven decision making is to build models whose outcomes do not discriminate against some demographic groups, including gender, ethnicity, or age. To ensure non-discrimination in learning tasks, knowledge of the sensitive attributes is essential, while, in practice, these attributes may not be available due to legal and ethical requirements. To address this challenge, this paper studies a model that protects the privacy of the individualsâ€™ sensitive information while also allowing it to learn non-discriminatory predictors. The method relies on the notion of differential privacy and the use of Lagrangian duality to design neural networks that can accommodate fairness constraints while guaranteeing the privacy of sensitive attributes. The paper analyses the tension between accuracy, privacy, and fairness and the experimental evaluation illustrates the benefits of the proposed model on several prediction tasks.

Differentially Private and Fair Deep Learning: A Lagrangian Dual Approach

We study the problem of fair and efficient allocation of a set of indivisible goods to agents with additive valuations using the popular fairness notions of envy-freeness up to one good (EF1) and equitability up to one good (EQ1) in conjunction with Pareto-optimality (PO). There exists a pseudo-polynomial time algorithm to compute an EF1+PO allocation, and a non-constructive proof of existence of allocations that are both EF1 and fractionally Pareto-optimal (fPO). We present a pseudo-polynomial time algorithm to compute an EF1+fPO allocation, thereby improving the earlier results. Our techniques also enable us to show that an EQ1+fPO allocation always exists when the values are positive, and that it can be computed in pseudo-polynomial time.

We also consider the class of k-ary instances where k is a constant, i.e., each agent has at most k different values for the goods. We show that for such instances an EF1+fPO allocation can be computed in polynomial time. When all values are positive, we show that an EQ1+fPO allocation for such instances can be computed in polynomial time. Next, we consider instances where the number of agents is constant, and show that an EF1+PO (also EQ1+PO) allocation can be computed in polynomial time. These results significantly extend the polynomial-time computability beyond the known cases of binary or identical valuations.

Further, we show that the problem of computing an EF1+PO allocation polynomial-time reduces to a problem in the complexity class PLS. We also design a polynomial-time algorithm that computes Nash welfare maximizing allocations when there are constantly many agents with constant many different values for the goods.

On Fair and Efficient Allocations of Indivisible Goods

A remarkable characteristic of overparameterized deep neural networks (DNNs) is that their accuracy does not degrade when the network width is increased. Recent evidence suggests that developing compressible representations allows the complexity of large networks to be adjusted for the learning task at hand. However, these representations are poorly understood. A promising strand of research inspired from biology involves studying representations at the unit level as it offers a more granular interpretation of the neural mechanisms. In order to better understand what facilitates increases in width without decreases in accuracy, we ask: Are there mechanisms at the unit level by which networks control their effective complexity? If so, how do these depend on the architecture, dataset, and hyperparameters? We identify two distinct types of â€œfrivolousâ€ units that proliferate when the networkâ€™s width increases: prunable units which can be dropped out of the network without significant change to the output and redundant units whose activities can be expressed as a linear combination of others. These units imply complexity constraints as the function the network computes could be expressed without them. We also identify how the development of these units can be influenced by architecture and a number of training factors. Together, these results help to explain why the accuracy of DNNs does not degrade when width is increased and highlight the importance of frivolous units toward understanding implicit regularization in DNNs.

Frivolous Units: Wider Networks Are Not Really That Wide

We propose two incremental preference elicitation methods for interactive preference-based optimization on weighted matroid structures. More precisely, for linear objective (utility) functions, we propose an interactive greedy algorithm interleaving preference queries with the incremental construction of an independent set to obtain an optimal or near-optimal base of a matroid. We also propose an interactive local search algorithm based on sequences of possibly improving exchanges for the same problem.  For both algorithms, we provide performance guarantees on the quality of the returned solutions and the number of queries.  Our algorithms are tested on the uniform, graphical and scheduling matroids to solve three different problems (committee election, spanning tree, and scheduling problems) and evaluated in terms of computation times, number of queries, and empirical error.

Combining Preference Elicitation with Local Search and Greedy Search for Matroid Optimization

Quantitative trading and investment decision making are intricate financial tasks that rely on accurate stock selection.
Despite advances in deep learning that have made significant progress in the complex and highly stochastic stock prediction problem, modern solutions face two significant limitations. 
They do not directly optimize the target of investment in terms of profit, and treat each stock as independent from the others, ignoring the rich signals between related stocks' temporal price movements.
Building on these limitations, we reformulate stock prediction as a learning to rank problem and propose STHAN-SR, a neural hypergraph architecture for stock selection.
The key novelty of our work is the proposal of modeling the complex relations between stocks through a hypergraph and a temporal Hawkes attention mechanism to tailor a new spatiotemporal attention hypergraph network architecture to rank stocks based on profit by jointly modeling stock interdependence and the temporal evolution of their prices.
Through experiments on three markets spanning over six years of data, we show that STHAN-SR significantly outperforms state-of-the-art neural stock forecasting methods.
We validate our design choices through ablative and exploratory analyses over STHAN-SR's spatial and temporal components and demonstrate its practical applicability.

Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach

Accurate reconstruction of static environments from LiDAR scans of scenes containing dynamic objects, which we refer to as Dynamic to Static Translation (DST), is an important area of research in Autonomous Navigation. This problem has been recently explored for visual SLAM, but to the best of our knowledge no work has been attempted to address DST for LiDAR scans. The problem is of critical importance due to wide-spread adoption of LiDAR in Autonomous Vehicles. We show that state-of the art methods developed for the visual domain when adapted for LiDAR scans perform poorly. We develop DSLR, a deep generative model which learns a mapping between dynamic scan to its static counterpart through an adversarially trained autoencoder. Our model yields the first solution for DST on LiDAR that generates static scans without using explicit segmentation labels. DSLR cannot always be applied to real world data due to lack of paired dynamic-static scans. Using Unsupervised Domain Adaptation, we propose DSLR-UDA for transfer to real world data and experimentally show that this performs well in real world settings. Additionally, if segmentation information is available, we extend DSLR to DSLR-Seg to further improve the reconstruction quality. DSLR gives the state of the art performance on simulated and real-world datasets and also shows at least 4Ã— improvement. We show that DSLR, unlike the existing baselines, is a practically viable model with its reconstruction quality within the tolerable limits for tasks pertaining to autonomous navigation like SLAM in dynamic environments.

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Faster and Better Simple Temporal Problems

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Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction

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Faster and Better Simple Temporal Problems

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Exploiting Contrastive Learning and Numerical Evidence for Confusing Legal Judgment Prediction

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PRESENTATIONS

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