Reviewerly/exHarmony huggingface dataset:
LATTE: A Framework for Learning Item-Features to Make a Domain-Expert for Effective Conversational Recommendation

For high-quality conversational recommender systems (CRS), it is important to recommend the suitable items by capturing the items' features mentioned in the dialog and to explain the appropriate ones among the various features of the recommended item. We argue that the CRS model should be a domain-expert who is (1) knowledgeable about the relationships between items and their various features and (2) able to explain the recommended item with its features relevant to dialog context. To this end, we propose a novel framework, named as LATTE, to pre-train each core module in CRS (i.e., the recommendation and the conversation module) through abundant external data. For the recommendation module, we pre-train the recommendation module to comprehensively understand the relationships between items and their various features by leveraging both multi-reviews and a knowledge graph. For pre-training the conversation module, we create the synthetic dialogs, which contain responses providing the explanation relevant to the dialog context by using all the items' features and dialog templates. Through extensive experiments on two public CRS datasets, we demonstrate that LATTE exhibits (1) the effectiveness of each module in LATTE, (2) the superiority over 7 state-of-the art methods, and (3) the interpretations based on visualization.
A Feature-Based Coalition Game Framework with Privileged Knowledge Transfer for User-tag Profile Modeling

User-tag profiling is an effective way of mining user attributes in modern recommender systems. However, prior researches fail to extract users' precise preferences for tags in the items due to their incomplete feature-input patterns. To convert user-item interactions to user-tag preferences, we propose a novel feature-based framework named Coalition Tag Multi-View Mapping (CTMVM), which identifies and investigates two special features, Coalition Feature and Privileged Feature. The former indicates decisive tags in each click where relationships between tags in one item are treated as a coalition game. The latter represents highly informative features that only occur during training. For the coalition feature, we adopt Shapley Value based Empowerment (SVE) to model the tags in items with a game-theoretic paradigm and charge the network to straight master user preferences for essential tags. For the privileged feature, we present Privileged Knowledge Mapping (PKM) to explicitly distill privileged feature knowledge for each tag into one single embedding, which assists the model in predicting user-tag preferences at a more fine-grained level. However, the barren capacity of single embeddings limits the diverse relations between each tag and different privileged features. Therefore, we further propose Adaptive Multi-View Mapping (AMVM) model to enhance effect by handling multiple mapping networks. Excellent offline experiment results on two public and one private datasets show the out-standing performance of CTMVM. After the deployment on Alibaba large-scale recommendation systems, CTMVM achieved improvement by 10.81% and 6.74% in terms of Theme-CTR and Item-CTR respectively, which validates the effectiveness of taking in the two particular features for training.
PASS: Personalized Advertiser-aware Sponsored Search

The nucleus of online sponsored search systems lies in measuring the relevance between the search intents of users and the advertising purposes of advertisers. Existing conventional doublet-based (query-keyword) relevance models solely rely on short queries and keywords to uncover such intents, which ignore the diverse and personalized preferences of participants (i.e., users and advertisers), resulting in undesirable advertising performance. In this paper, we investigate the novel problem of Personalized A dvertiser-aware Sponsored Search (PASS). Our motivation lies in incorporating the portraits of users and advertisers into relevance models to facilitate the modeling of intrinsic search intents and advertising purposes, leading to a quadruple-based (i.e., user-query-keyword-advertiser) task. Various types of historical behaviors are explored in the format of hypergraphs to provide abundant signals on identifying the preferences of participants. A novel heterogeneous textual hypergraph transformer is further proposed to deeply fuse the textual semantics and the high-order hypergraph topology. Our proposal is extensively evaluated over real industry datasets, and experimental results demonstrate its superiority.
Multi-Row, Multi-Span Distant Supervision For Table+Text Question Answering

Question answering (QA) over tables and linked text, also called TextTableQA, has witnessed significant research in recent years, as tables are often found embedded in documents along with related text.HybridQA and OTT-QA are the two best-known Text-TableQA datasets, with questions that are best answered by combining information from both table cells and linked text passages.A common challenge in both datasets, and TextTableQA in general, is that the training instances include just the question and answer, where the gold answer may match not only multiple table cells across table rows but also multiple text spans within the scope of a table row and its associated text.This leads to a noisy multiinstance training regime.We present MITQA, a transformer-based TextTableQA system that is explicitly designed to cope with distant supervision along both these axes, through a multiinstance loss objective, together with careful curriculum design.Our experiments show that the proposed multi-instance distant supervision approach helps MITQA get sate-of-the-art results beating the existing baselines for both Hy-bridQA and OTT-QA, putting MITQA at the top of HybridQA leaderboard with best EM and F1 scores on a held out test set.
DIONYSUS: A Pre-trained Model for Low-Resource Dialogue Summarization

Dialogue summarization has recently garnered significant attention due to its wide range of applications.However, existing methods for summarizing dialogues have limitations because they do not take into account the inherent structure of dialogue and rely heavily on labeled data, which can lead to poor performance in new domains.In this work, we propose DIONYSUS (dynamic input optimization in pre-training for dialogue summarization), a pre-trained encoder-decoder model for summarizing dialogues in any new domain.To pretrain DIONYSUS, we create two pseudo summaries for each dialogue example: one from a fine-tuned summarization model and the other from important dialogue turns.We then choose one of these pseudo summaries based on information distribution differences in different types of dialogues.This selected pseudo summary serves as the objective for pre-training DIONYSUS using a self-supervised approach on a large dialogue corpus.Our experiments show that DIONYSUS outperforms existing methods on six datasets, as demonstrated by its ROUGE scores in zero-shot and few-shot settings.
Sheffield’s Submission to the AmericasNLP Shared Task on Machine Translation into Indigenous Languages

In this paper we describe the University of Sheffield's submission to the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages which comprises the translation from Spanish to eleven indigenous languages.Our approach consists of extending, training, and ensembling different variations of NLLB-200.We use data provided by the organizers and data from various other sources such as constitutions, handbooks, news articles, and backtranslations generated from monolingual data.On the dev set, our best submission outperforms the baseline by 11% average chrF across all languages, with substantial improvements particularly for Aymara, Guarani and Quechua.On the test set, we achieve the highest average chrF of all the submissions, we rank first in four of the eleven languages, and at least one of our submissions ranks in the top 3 for all languages. 1
Generic Temporal Reasoning with Differential Analysis and Explanation

Temporal reasoning is the task of predicting temporal relations of event pairs.While temporal reasoning models can perform reasonably well on in-domain benchmarks, we have little idea of these systems' generalizability due to existing datasets' limitations.In this work, we introduce a novel task named TODAY that bridges this gap with temporal differential analysis, which as the name suggests, evaluates whether systems can correctly understand the effect of incremental changes.Specifically, TODAY introduces slight contextual changes for given event pairs, and systems are asked to tell how this subtle contextual change would affect relevant temporal relation distributions.To facilitate learning, TODAY also annotates human explanations.We show that existing models, including GPT-3.5, drop to random guessing on TODAY, suggesting that they heavily rely on spurious information rather than proper reasoning for temporal predictions.On the other hand, we show that TODAY's supervision style and explanation annotations can be used in joint learning, encouraging models to use more appropriate signals during training and thus outperform across several benchmarks.TODAY can also be used to train models to solicit incidental supervision from noisy sources such as GPT-3.5, thus moving us more toward the goal of generic temporal reasoning systems.
PROSE: Graph Structure Learning via Progressive Strategy

Graph Neural Networks (GNNs) have been a powerful tool to acquire high-quality node representations dealing with graphs, which strongly depends on a promising graph structure. In the real world scenarios, it is inevitable to introduce noises in graph topology. To prevent GNNs from the disturbance of irrelevant edges or missing edges, graph structure learning is proposed and has attracted considerable attentions in recent years. In this paper, we argue that current graph structure learning methods still pay no regard to the status of nodes and just judge all of their connections simultaneously using a monotonous standard, which will lead to indeterminacy and instability in the optimization process. We designate these methods as status-unaware models. To demonstrate the rationality of our point of view, we conduct exploratory experiments on publicly available datasets, and discover some exciting observations. Afterwards, we propose a new model named Graph Structure Learning via Progressive Strategy (PROSE) according to the observations, which uses a progressive strategy to acquire ideal graph structure in a status-aware way. Concretely, PROSE consists of progressive structure splitting module (PSS) and progressive structure refining module (PSR) to modify node connections according to their global potency, and we also introduce horizontal position encoding and vertical position encoding in order to capture fruitful graph topology information ignored by previous methods. On several widely-used graph datasets, we conduct extensive experiments to demonstrate the effectiveness of our model, and the source code 1 https://github.com/tigerbunny2023/PROSE is provided.
VisKoP: Visual Knowledge oriented Programming for Interactive Knowledge Base Question Answering

We present Visual Knowledge oriented Programming platform (VisKoP), a knowledge base question answering (KBQA) system that integrates human into the loop to edit and debug the knowledge base (KB) queries.VisKoP not only provides a neural program induction module, which converts natural language questions into knowledge oriented program language (KoPL), but also maps KoPL programs into graphical elements.KoPL programs can be edited with simple graphical operators, such as "dragging" to add knowledge operators and "slot filling" to designate operator arguments.Moreover, VisKoP provides auto-completion for its knowledge base schema and users can easily debug the KoPL program by checking its intermediate results.To facilitate the practical KBQA on a million-entity-level KB, we design a highly efficient KoPL execution engine for the back-end.Experiment results show that VisKoP is highly efficient and user interaction can fix a large portion of wrong KoPL programs to acquire the correct answer.The VisKoP online demo 1 , highly efficient KoPL engine 2 , and screencast video 3 are now publicly available.
AliBERT: A Pre-trained Language Model for French Biomedical Text

Over the past few years, domain-specific pretrained language models have been investigated and have shown remarkable achievements in different downstream tasks, especially in biomedical domain.These achievements stem on the well-known BERT architecture which uses an attention-based selfsupervision for context learning of textual documents.However, these domain-specific biomedical pre-trained language models mainly use English corpora.Therefore, non-English, domain-specific pre-trained models remain quite rare, both of these requirements being hard to achieve.In this work, we proposed AliBERT, a biomedical pre-trained language model for French and investigated different learning strategies.AliBERT is trained using regularized Unigram based tokenizer trained for this purpose.AliBERT has achieved stateof-the-art F1 and accuracy scores in different down-stream biomedical tasks.Our pre-trained model manages to outperform some French non domain-specific models such as Camem-BERT and FlauBERT on diverse down-stream tasks, with less pre-training and training time and with much smaller corpora.
Automatic Term and Sentence Classification Via Augmented Term and Pre-trained language model in ESG Taxonomy texts

In this paper, we present our solutions to the Fin-Sim4 shared task which is co-located with the FinNLP workshop at IJCAI-2022.This new edition of FinSim4-ESG is extended to the "Environment, Social and Governance (ESG)" related issues in the financial domain.There are two sub-tasks in the FinSim4 shared task.The goal of sub-task1 is to develop a model to classify correctly a list of given terms from ESG taxonomy domain into the most relevant concepts.The aim of sub-task2 is to design a system that can automatically classify the ESG Taxonomy text sentences into sustainable or unsustainable class.We have developed several classifiers to automatically predict the concepts of terms with augmented terms and word vectors and classify sentences into sustainable or unsustainable label with pre-trained language models.The final result leaderboard shows that our proposed methods yield a significant performance improvement compared to the baseline which ranked 1st in the sub-task2 and 2rd (Mean Rank) in the sub-task1.
Inducing Generalizable and Interpretable Lexica

Lexica -words and associated scores -are widely used as simple, interpretable, generalizable language features to predict sentiment, emotions, mental health, and personality.They also provide insight into the psychological features behind those moods and traits.Such lexica, historically created by human experts, are valuable to linguists, psychologists, and social scientists, but they take years of refinement and have limited coverage.In this paper, we investigate how the lexica that provide psycholinguistic insights could be computationally induced and how they should be assessed.We identify generalizability and interpretability as two essential properties of such lexica.We induce lexica using both context-oblivious and context-aware approaches, compare their predictive performance both within the training corpus and across various corpora, and evaluate their quality using crowd-worker assessment.We find that lexica induced from contextoblivious models are more generalizable and interpretable than those from more accurate context-aware transformer models.In addition, lexicon scores can identify explanatory words more reliably than a high performing transformer with feature-importance measures like SHAP. 1
HomoGCL: Rethinking Homophily in Graph Contrastive Learning

Contrastive learning (CL) has become the de-facto learning paradigm in self-supervised learning on graphs, which generally follows the "augmenting-contrasting'' learning scheme. However, we observe that unlike CL in computer vision domain, CL in graph domain performs decently even without augmentation. We conduct a systematic analysis of this phenomenon and argue that homophily, i.e., the principle that "like attracts like'', plays a key role in the success of graph CL. Inspired to leverage this property explicitly, we propose HomoGCL, a model-agnostic framework to expand the positive set using neighbor nodes with neighbor-specific significances. Theoretically, HomoGCL introduces a stricter lower bound of the mutual information between raw node features and node embeddings in augmented views. Furthermore, HomoGCL can be combined with existing graph CL models in a plug-and-play way with light extra computational overhead. Extensive experiments demonstrate that HomoGCL yields multiple state-of-the-art results across six public datasets and consistently brings notable performance improvements when applied to various graph CL methods. Code is avilable at https://github.com/wenzhilics/HomoGCL.
Empower Post-hoc Graph Explanations with Information Bottleneck: A Pre-training and Fine-tuning Perspective

Researchers recently investigated to explain Graph Neural Networks (GNNs) on the access to a task-specific GNN, which may hinder their wide applications in practice. Specifically, task-specific explanation methods are incapable of explaining pretrained GNNs whose downstream tasks are usually inaccessible, not to mention giving explanations for the transferable knowledge in pretrained GNNs. Additionally, task-specific methods only consider target models' output in the label space, which are coarse-grained and insufficient to reflect the model's internal logic. To address these limitations, we consider a two-stage explanation strategy, i.e., explainers are first pretrained in a task-agnostic fashion in the representation space and then further fine-tuned in the task-specific label space and representation space jointly if downstream tasks are accessible. The two-stage explanation strategy endows post-hoc graph explanations with the applicability to pretrained GNNs where downstream tasks are inaccessible and the capacity to explain the transferable knowledge in the pretrained GNNs. Moreover, as the two-stage explanation strategy explains the GNNs in the representation space, the fine-grained information in the representation space also empowers the explanations. Furthermore, to achieve a trade-off between the fidelity and intelligibility of explanations, we propose an explanation framework based on the Information Bottleneck principle, named Explainable Graph Information Bottleneck (EGIB). EGIB subsumes the task-specific explanation and task-agnostic explanation into a unified framework. To optimize EGIB objective, we derive a tractable bound and adopt a simple yet effective explanation generation architecture. Based on the unified framework, we further theoretically prove that task-agnostic explanation is a relaxed sufficient condition of task-specific explanation, which indicates the transferability of task-agnostic explanations. Extensive experimental results demonstrate the effectiveness of our proposed explanation method.
Learning to Relate to Previous Turns in Conversational Search

Conversational search allows a user to interact with a search system in multiple turns. A query is strongly dependent on the conversation context. An effective way to improve retrieval effectiveness is to expand the current query with historical queries. However, not all the previous queries are related to, and useful for expanding the current query. In this paper, we propose a new method to select relevant historical queries that are useful for the current query. To cope with the lack of labeled training data, we use a pseudo-labeling approach to annotate useful historical queries based on their impact on the retrieval results. The pseudo-labeled data are used to train a selection model. We further propose a multi-task learning framework to jointly train the selector and the retriever during fine-tuning, allowing us to mitigate the possible inconsistency between the pseudo labels and the changed retriever. Extensive experiments on four conversational search datasets demonstrate the effectiveness and broad applicability of our method compared with several strong baselines.
Interdependent Causal Networks for Root Cause Localization

The goal of root cause analysis is to identify the underlying causes of system problems by discovering and analyzing the causal structure from system monitoring data. It is indispensable for maintaining the stability and robustness of large-scale complex systems. Existing methods mainly focus on the construction of a single effective isolated causal network, whereas many real-world systems are complex and exhibit interdependent structures (i.e., multiple networks of a system are interconnected by cross-network links). In interdependent networks, the malfunctioning effects of problematic system entities can propagate to other networks or different levels of system entities. Consequently, ignoring the interdependency results in suboptimal root cause analysis outcomes.
From Human Days to Machine Seconds: Automatically Answering and Generating Machine Learning Final Exams

A final exam in machine learning at a top institution such as MIT, Harvard, or Cornell typically takes faculty days to write, and students hours to solve. We demonstrate that large language models pass machine learning finals at a human level on finals available online and automatically generate new human-quality final exam questions in seconds. Previous work has developed program synthesis and few-shot learning methods to solve university-level problem set questions in mathematics and STEM courses. In this work, we develop and compare methods that solve final exams, which differ from problem sets in several ways: the questions are longer, have multiple parts, are more complicated, and span a broader set of topics. We curate a dataset and benchmark of questions from machine learning final exams available online and code for answering these questions and generating new questions. We show how to generate new questions from other questions and course notes. For reproducibility and future research on this final exam benchmark, we use automatic checkers for multiple-choice, numeric, and questions with expression answers. A student survey comparing the quality, appropriateness, and difficulty of machine-generated questions with human-written questions shows that across multiple aspects, machine-generated questions are indistinguishable from human-generated questions and are suitable for final exams. We perform ablation studies comparing zero-shot learning with few-shot learning and chain-of-thought prompting using GPT-3, OPT, Codex, and ChatGPT across machine learning topics and find that few-shot learning methods perform best. We highlight the transformative potential of language models to streamline the writing and solution of large-scale assessments, significantly reducing the workload from human days to mere machine seconds. Our results suggest that rather than banning large language models such as ChatGPT in class, instructors should teach students to harness them by asking students meta-questions about correctness, completeness, and originality of the responses generated, encouraging critical thinking in academic studies.