Dominican Republic

Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks. However, it usually involves multiple-step pipelines that propagate errors or are limited to a small number of relation types. To overcome these issues, we propose the use of autoregressive seq2seq models. Such models have previously been shown to perform well not only in language generation, but also in NLU tasks such as Entity Linking, thanks to their framing as seq2seq tasks.
In this paper, we show how Relation Extraction can be simplified by expressing triplets as a sequence of text and we present REBEL, a seq2seq model based on BART that performs end-to-end relation extraction for more than 200 different relation types. We show our model&#39;s flexibility by fine-tuning it on an array of Relation Extraction and Relation Classification benchmarks, with it attaining state-of-the-art performance in most of them.

EMNLP 2021

REBEL: Relation Extraction By End-to-end Language generation

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

Extracting relation triplets from raw text is a crucial task in Information Extraction, enabling multiple applications such as populating or validating knowledge bases, factchecking, and other downstream tasks. However, it usually involves multiple-step pipelines that propagate errors or are limited to a small number of relation types. To overcome these issues, we propose the use of autoregressive seq2seq models. Such models have previously been shown to perform well not only in language generation, but also in NLU tasks such as Entity Linking, thanks to their framing as seq2seq tasks.
In this paper, we show how Relation Extraction can be simplified by expressing triplets as a sequence of text and we present REBEL, a seq2seq model based on BART that performs end-to-end relation extraction for more than 200 different relation types. We show our model's flexibility by fine-tuning it on an array of Relation Extraction and Relation Classification benchmarks, with it attaining state-of-the-art performance in most of them.

technical paper

Knowledge representation learning (KRL) has been used in plenty of knowledge-driven tasks. Despite fruitfully progress, existing methods still suffer from the immaturity on tackling potentially-imperfect knowledge graphs and highly-imbalanced positive-negative instances during training, both of which would hinder the performance of KRL. In this paper, we propose Contrastive Completion Coding ($C^3$), a novel KRL framework that is composed of two functional components:
\textbf{1.} Hierarchical Architecture, which integrates both low-level standalone features and high-level topology-aware features to yield robust embedding for each entity/relation.
\textbf{2.} Normalized Contrasitive Training, which conducts normalized one-to-many contrasitive learning to emphasize different negatives with different weights, delivering better convergence compared to conventional training losses. 
Extensive experiments on several benchmarks verify the efficacy of the two proposed techniques and combing them together generally achieves superior performance against state-of-the-art approaches.

Knowledge Representation Learning with Contrastive Completion Coding

Analyzing microblogs where we post what we experience enables us to perform various applications such as social-trend analysis and entity recommendation.
To track emerging trends in a variety of areas, we want to categorize information on emerging entities (e.g., Avatar 2) in microblog posts according to their types (e.g., Film).
We thus introduce a new entity typing task that assigns a fine-grained type to each emerging entity when a burst of posts containing that entity is first observed in a microblog.
The challenge is to perform typing from noisy microblog posts without relying on prior knowledge of the target entity.
To tackle this task, we build large-scale Twitter datasets for English and Japanese using time-sensitive distant supervision.
We then propose a modular neural typing model that encodes not only the entity and its contexts but also meta information in multiple posts. 
To type `homographic' emerging entities (e.g., `Go' means an emerging programming language and a classic board game), which contexts are noisy, we devise a context selector that finds related contexts of the target entity.
Experiments on the Twitter datasets confirm the effectiveness of our typing model and the context selector.

Fine-grained Typing of Emerging Entities in Microblogs

Short text nowadays has become a more fashionable form of text data, e.g., Twitter posts, news titles, and product reviews. Extracting semantic topics from short texts plays a significant role in a wide spectrum of NLP applications, and neural topic modeling is now a major tool to achieve it. Motivated by learning more coherent and semantic topics, in this paper we develop a novel neural topic model named Dual Word Graph Topic Model (DWGTM), which extracts topics from simultaneous word co-occurrence and semantic correlation graphs. To be specific, we learn word features from the global word co-occurrence graph, so as to ingest rich word co-occurrence information; we then generate text features with word features, and feed them into an encoder network to get topic proportions per-text; finally, we reconstruct texts and word co-occurrence graph with topical distributions and word features, respectively. Besides, to capture semantics of words, we also apply word features to reconstruct a word semantic correlation graph computed by pre-trained word embeddings. Upon those ideas, we formulate DWGTM in an auto-encoding paradigm and efficiently train it with the spirit of neural variational inference. Empirical results validate that DWGTM can generate more semantically coherent topics than baseline topic models.

Extracting Topics with Simultaneous Word Co-occurrence and Semantic Correlation Graphs: Neural Topic Modeling for Short Texts

Freedom of the press and media is of vital importance for democratically organised states and open societies. We introduce the Press Freedom Monitor, a tool that aims to detect reported press and media freedom violations in news articles and tweets. It is used by press and media freedom organisations to support their daily monitoring and to trigger rapid response actions. The Press Freedom Monitor enables the monitoring experts to get a swift overview of recently reported incidents and it has performed impressively in this regard. This paper presents our work on the tool, starting with the training phase, which comprises defining the topic-related keywords to be used for querying APIs for news and Twitter content and evaluating different machine learning models based on a training dataset specifically created for our use case. Then, we describe the components of the production pipeline, including data gathering, duplicates removal, country mapping, case mapping and the user interface. We also conducted a usability study to evaluate the effectiveness of the user interface, and describe improvement plans for future work.

Press Freedom Monitor: Detection of Reported Press and Media Freedom Violations in Twitter and News Articles

Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA), where latent representations of questions and passages are exploited for maximum inner product search in the retrieval process. However, current dense retrievers require splitting documents into short passages that usually contain local, partial and sometimes biased context, and highly depend on the splitting process. As a consequence, it may yield inaccurate and misleading hidden representations, thus deteriorating the final retrieval result. In this work, we propose Dense Hierarchical Retrieval (DHR), a hierarchical framework which can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic semantics specific to each passage. Specifically, a document-level retriever first identifies relevant documents, among which relevant passages are then retrieved by a passage-level retriever. The ranking of the retrieved passages will be further calibrated by examining the document-level relevance. In addition, hierarchical title structure and two negative sampling strategies (i.e., In-Doc and In-Sec negatives) are investigated. We apply DHR to large-scale open-domain QA datasets. DHR significantly outperforms the original dense passage retriever, and helps an end-to-end QA system outperform the strong baselines on multiple open-domain QA benchmarks.

Dense Hierarchical Retrieval for Open-domain Question Answering

The availability of parallel sentence simplification (SS) is scarce for neural SS modelings. We propose an unsupervised method to build SS corpora from large-scale bilingual translation corpora, alleviating the need for SS supervised corpora. Our method is motivated by the following two findings: neural machine translation model usually tends to generate more high-frequency tokens and the difference of text complexity levels exists between the source and target language of a translation corpus. By taking the pair of the source sentences of translation corpus and the translations of their references in a bridge language, we can construct large-scale pseudo parallel SS data. Then, we keep these sentence pairs with a higher complexity difference as SS sentence pairs. The building SS corpora with an unsupervised approach can satisfy the expectations that the aligned sentences preserve the same meanings and have difference in text complexity levels. Experimental results show that SS methods trained by our corpora achieve the state-of-the-art results and significantly outperform the results on English benchmark WikiLarge.

An Unsupervised Method for Building Sentence Simplification Corpora in Multiple Languages

Cross-lingual Sentence Retrieval (CLSR) aims at retrieving parallel sentence pairs that are translations of each other from a multilingual set of comparable documents. The retrieved parallel sentence pairs can be used in other downstream NLP tasks such as machine translation and cross-lingual word sense disambiguation. We propose a CLSR framework called Robust Fragment-level Representation (RFR) CLSR framework to address Out-of-Domain (OOD) CLSR problems. In particular, we improve the sentence retrieval robustness by representing each sentence as a collection of fragments. In this way, we change the retrieval granularity from the sentence to the fragment level. We performed CLSR experiments based on three OOD datasets, four language pairs, and three base well-known sentence encoders: m-USE, LASER, and LaBSE. Experimental results show that RFR significantly improves the base encoders’ performance for more than 85% of the cases.

Robust Fragment-Based Framework for Cross-lingual Sentence Retrieval

Query Rewriting (QR) is proposed to solve the problem of the word mismatch between queries and documents in Web search.
Existing approaches usually model QR with an end-to-end sequence-to-sequence (seq2seq) model. The state-of-the-art Transformer-based models can effectively learn textual semantics from user session logs, but they often ignore users' geographic location information that is crucial for the Point-of-Interest (POI) search of map services. In this paper, we proposed a pre-training model, called Geo-BERT, to integrate semantics and geographic information in the pre-trained representations of POIs. Firstly, we simulate POI distribution in the real world as a graph, in which nodes represent POIs and multiple geographic granularities. Then we use graph representation learning methods to get geographic representations. Finally, we train a BERT-like pre-training model with text and POIs’ graph embeddings to get an integrated representation of both geographic and semantic information, and apply it in the QR of POI search. The proposed model achieves excellent accuracy on a wide range of real-world datasets of map services.

Geo-BERT Pre-training Model for Query Rewriting in POI Search

Recently, sponsored search has become one of the most lucrative channels for marketing. 
As the fundamental basis of sponsored search, relevance modeling has attracted increasing attention due to the tremendous practical value. 
Most existing methods solely rely on the query-keyword pairs. 
However, keywords are usually short texts with scarce semantic information, which may not precisely reflect the underlying advertising intents. 
In this paper, we investigate the novel problem of advertiser-aware relevance modeling, which leverages the advertisers' information to bridge the gap between the search intents and advertising purposes. 
Our motivation lies in incorporating the unsupervised bidding behaviors as the complementary graphs to learn desirable advertiser representations. 
We further propose a Bidding-Graph augmented Triple-based Relevance model BGTR with three towers to deeply fuse the bidding graphs and semantic textual data. 
Empirically, we evaluate the BGTR model over a large industry dataset, and the experimental results consistently demonstrate its superiority.

Leveraging Bidding Graphs for Advertiser-Aware Relevance Modeling in Sponsored Search

Knowledge graph entity typing aims to infer entities' missing types in knowledge graphs which is an important but under-explored issue. This paper proposes a novel method for this task by utilizing entities' contextual information. Specifically, we design two inference mechanisms: i) N2T: independently use each neighbor of an entity to infer its type; ii) Agg2T: aggregate the neighbors of an entity to infer its type. Those mechanisms will produce multiple inference results, and an exponentially weighted pooling method is used to generate the final inference result. Furthermore, we propose a novel loss function to alleviate the false-negative problem during training. Experiments on two real-world KGs demonstrate the effectiveness of our method. The source code and data of this paper can be obtained from https://github.com/CCIIPLab/CET.

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Knowledge Representation Learning with Contrastive Completion Coding

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