Iterative Document-level Information Extraction via Imitation Learning (2023)

Abstract: We present a novel iterative extraction model, IterX, for extracting complex relations, or templates, i.e., N-tuples representing a mapping from named slots to spans of text within a document. Documents may feature zero or more instances of a template of any given type, and the task of template extraction entails identifying the templates in a document and extracting each template’s slot values. Our imitation learning approach casts the problem as a Markov decision process (MDP), and relieves the need to use predefined template orders to train an extractor. It leads to state-of-the-art results on two established benchmarks – 4-ary relation extraction on SciREX and template extraction on MUC-4 – as well as a strong baseline on the new BETTER Granular task.

## introduction a variety of tasks in information extraction (ie) require synthesizing information across multiple sentences, up to the length of an entire document. the centrality of document-level reasoning to ie has been underscored by an intense research focus in recent years on problems such as argument linking @xcite , 𝑁-ary relation extraction @xcite @xcite , and -our primary focus -template extraction @xcite . construed broadly, template extraction is general enough to subsume certain other document-level extraction tasks, including 𝑁-ary relation extraction. motivated by this consideration, we propose to treat these problems under a unified framework of generalized template extraction ( §2). 2 figure 1 shows 4-ary relations from the scirex dataset @xcite , presented as simple templates. in this paper, we propose a multi-sentiment-resource enhanced attention network (mean)… despite the remarkable progress made by the previous work, we argue that sentiment analysis still remains a challenge… movie review (mr) and stanford sentiment treebank (sst) are used to evaluate our model… we adopt classification accuracy as the evaluation metric. template 1 task method dataset metric sentiment analysis mean movie review accuracy template 2 task method dataset metric sentiment analysis mean sst accuracy since documents typically describe multiple complex events and relations, template extraction systems must be capable of predicting multiple templates per document. existing approaches such as @xcite and @xcite rely on a linearization strategy to force models to learn to predict templates in a pre-defined order. in general, however, such orderings are arbitrary. others have instead focused on the simplified problem of role-filler entity extraction (ree), which entails extracting all slot-filling entities but does not involve mapping them to individual templates @xcite . we present a new model for generalized template extraction, iterx, that iteratively extracts multiple templates from a document without requiring a pre-defined linearization scheme. we formulate the problem as a markov decision process (mdp, §2), where an action corresponds to the generation of a single template ( §3.2), and states are sets of predicted templates ( §3.3). our system is trained via imitation learning, where the agent imitates a dynamic oracle drawn from an expert policy ( §3.4). our contributions can be summarized as follows: col. isaacs said that the guerrillas attacked the "la eminencia" farm located near the "santo tomas" farm, where they burned the facilities and stole food he also reported that the guerrillas killed a peasant in the city of flores, in the northern el petén department, and burned a tank truck. template 1 perpetratorindiv. prepetratororg. physicaltarget weapon victim type: arson guerrillas facilities --template 2 perpetratorindiv. prepetratororg. physicaltarget weapon victim type: arson guerrillas tank truck --in early september, illegal border crossings by two people infected with covid-19, triggered a week-long lockdown of another yunnan border city, ruili, and prompted at least eight prefectures and 25 counties to enter "wartime status." following the incident, yunnan vowed to strengthen border patrols. twenty people have been sentenced to prison in southwest china's yunnan province for crimes relating to illegal immigration from myanmar to china during the covid-19 pandemic, yunnan's high people's court reported on september 28. template 1 npi-events disease infectedcount outbreakevent where type: epidemic lockdown enter "wartime status" covid-19 two people pandemic ruili at least eight prefectures 25 counties template 2 origin destination ouctome totaldisplacedcount when overtime type: displacement myanmar china sentenced to prison twenty people -true figure 2 : template examples from muc-4 (left) and better granular (right) datasets. event triggers (e.g. burned above) are not annotated in muc-4 and are highlighted here only for clarity. we define a template ontology as a set of template types t , where each type 𝑡 ∈ t is associated with a set of slot types 𝑆 𝑡 . a template instance is defined as a pair (𝑡, {(𝑠 𝑘 : 𝑋 𝑘 ), • • • }) where 𝑡 ∈ t is a template type, 𝑠 𝑘 ∈ 𝑆 𝑡 is a slot type associated with 𝑡, and 𝑋 𝑘 ⊆ x is a subset of all mention spans extracted from the document that fills slot type 𝑠 𝑘 (𝑋 𝑘 = ∅ indicates that slot 𝑠 𝑘 has no filler). 3 taking figure 2 (left) as an example, template 1 has type 𝑡 = arson and slots {perpretratorindiv : {"guerrillas"}, physicaltarget : {"facilities"}}. we reduce the problem of extracting a single template to the problem of assigning a slot type to each extracted span 𝑥 𝑖 ∈ x , where some spans may be assigned a special null type (𝜀), indicating that they fill no slot in the current template. given this formulation, we can equivalently specify a template instance as (𝑡, 𝑎) where 𝑎 is an assignment of spans to slot types: {𝑥 𝑖 : 𝑠 𝑖 } 𝑠 𝑖 ∈𝑆 𝑡 . we denote the union of all slot types across all template types, along with the empty slot type 𝜀, as @xmath0. with these definitions in hand, the problem of generalized template extraction can be stated succinctly: given a template ontology t , a document 𝐷, and a set of candidate mentions x extracted from 𝐷, generate a set of template instances {(𝑡 1 , 𝑎 1 ), • • • , (𝑡 𝐾 , 𝑎 𝐾 )}, where 𝑡 𝑖 ∈ t . as an mdp we treat generalized template extraction as a markov decision process (mdp), where each step of the process generates one whole template instance. for simplicity, we consider the problem of extracting templates of a specific type 𝑡 ∈ t ; extracting all templates then simply requires iterating over t , where |t | is typically very small. this mdp (2 a , a, 𝐸, 𝑅) comprises the following: 4 here, each transition simply adds a generated template to the set of all templates generated for the document: 𝐸 ( 𝐴, 𝑎) = 𝐴 ∪ {𝑎}; these components are detailed in the following section. figure 3 shows iterx in action: the mdp 4 our notation is consistent with prior nlp work that uses mdps, e.g., levenshtein transformers @xcite . bidaf machine comprehension squad cnn/dailymail question answering exact match accuracy bidaf machine comprehension squad cnn/dailymail question answering exact match accuracy bidaf machine comprehension squad cnn/dailymail question answering exact match accuracy template 2 task method dataset metric machine comprehension bidaf cnn/dailymail accuracy x (0) x (1) x (2) ⇡ ⇡ ⇡ ⌧ ⌧ template 1 task method dataset metric question answering machine comprehension bidaf squad exact match x a x 0 ⇡ ⌧ policy transition generated template span state next state one iteration step extracted templates: produces two templates sequentially, terminating on a null assignment to all input spans in x . ## model our iterx model is a parameterized agent that makes decisions under the mdp above: conditioned on an input document 𝐷, spans x extracted from 𝐷, and a specific template type 𝑡, iterx generates a single template of type 𝑡 at each step. the model consists of two parameterized components: iterx generates a sequence of templates: it starts with initial state x (0) (see figure 3 for a running example) comprising only span representations derived from the encoder (as no templates have been predicted) and ends when no new template is generated ( §3.5). iterx is trained via imitation learning, aiming to imitate an expert policy 𝜋 * derived from reference templates. ## experiments we evaluate iterx on three datasets: scirex @xcite , muc-4 @xcite , and better phase ii english granular. foot_6 scirex is a challenge dataset for 4-ary relation extraction foot_7 on full academic articles related to machine learning. muc-4 and granular are both traditional template extraction tasks, though they differ in important respects, which we discuss in appendix c. for summary statistics, see however, we argue that neither ceaf-ree def nor ceaf-ree impl is consistent with historical evaluation of template extraction systems. ceaf-ree def errs in enforcing the entity alignment constraint: doing so effectively requires systems to perform coreference resolution, which is too strict and runs contrary to the original muc-4 evaluation. by contrast, ceaf-ree impl also errs in treating the template type as just another slot: this elides the important distinction between the kind of event being described and the participants in that event ( §6). in the interest of clarity, we define a modified version of the ceaf-ree metric that avoids both pitfalls: it relaxes the entity alignment constraint and it does not include template type in cross-slot averages. we call this version ceaf-rme, where "m" stands for mention and emphasizes the focus on mention-level rather than entity-level ("e") scoring. intuitively, relaxing this constraint amounts to placing the burden of coreference resolution on the metric: if the scorer aligns two predicted mentions to the same reference entity, the mentions are implicitly deemed coreferent. note that for a ceaf-family metric, the similarity function for entities 𝜙(𝑅, 𝑆) between the reference entity 𝑅 and the predicted 𝑆 is arbitrary @xcite . in du et al. (2021a) we argue that 𝜙 ⊆ overly penalizes models for predicting incorrect mentions, as even a single incorrect mention reduces the score to 0. instead, a better choice is 𝜙 3 (𝑅, 𝑆) = |𝑅 ∩ 𝑆| from @xcite : this computes a micro-average score of all mentions, and it adequately assigns partial credit to the overlap between the predicted mention set and the reference mention set. see figure 4 for a succinct comparison among these variants. 13 a more detailed discussion can be found in appendix d. for scirex, we report ceaf-rme (under both 𝜙 3 and 𝜙 ⊆ ). for muc-4, we report all metrics so that fair comparison with prior work can be made. for better granular, we use its official metrics, described in appendix d. ## conclusion we have presented iterx, a new model for generalized template extraction that iteratively generates templates via a markov decision process. iterx demonstrates state-of-the-art performance on two benchmarks in this domain -4-ary relation extraction on scirex and template extraction on muc-4 -and establishes a strong baseline on a third benchmark, better granular. in our experiments, we have also shown that imitation learning is a viable paradigm for these problems. we hope that our findings encourage future work to confront the challenge of dealing with documents that describe multiple complex events and relations head-on, rather than veiling this difficulty behind simplified task formulations.